{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
     ]
    }
   ],
   "source": [
    "nhefs_all = pd.read_excel('NHEFS.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['age', 'wt71', 'smokeintensity', 'smokeyrs']:\n",
    "    nhefs_all['{}^2'.format(col)] = nhefs_all[col] * nhefs_all[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhefs_all['one'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_dummies = pd.get_dummies(nhefs_all.education, prefix='edu')\n",
    "exercise_dummies = pd.get_dummies(nhefs_all.exercise, prefix='exercise')\n",
    "active_dummies = pd.get_dummies(nhefs_all.active, prefix='active')\n",
    "\n",
    "nhefs_all = pd.concat(\n",
    "    [nhefs_all, edu_dummies, exercise_dummies, active_dummies],\n",
    "    axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1629, 80)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhefs_all.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 17.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number that died during follow-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      # died:  318\n",
      "# didn't die: 1311\n"
     ]
    }
   ],
   "source": [
    "death = nhefs_all.death\n",
    "print(\"      # died: {:>4}\".format(death.sum()))\n",
    "print(\"# didn't die: {:>4}\".format((1 - nhefs_all.death).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of deaths for untreated and treated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>qsmk</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>death</th>\n",
       "      <td>216</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "qsmk     0    1\n",
       "death  216  102"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhefs_all.groupby('qsmk').agg({'death': 'sum'}).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the first and last dates of recorded death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first death: Jan 08, 1983\n",
      " last death: Dec 12, 1992\n"
     ]
    }
   ],
   "source": [
    "date_death = 10000 * nhefs_all.yrdth + 100 * nhefs_all.modth + nhefs_all.dadth\n",
    "date_death = pd.to_datetime(date_death, format=\"%y%m%d\")\n",
    "print(\"first death: {}\".format(date_death.min().strftime(\"%b %d, %Y\")))\n",
    "print(\" last death: {}\".format(date_death.max().strftime(\"%b %d, %Y\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Program 17.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add `longevity` and `survived` to the data set\n",
    "\n",
    "The `longevity` will be the number of months of follow-up lived until death. If the individual did not die, the value is set to the number of follow-up months, 120."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhefs_all['longevity'] = (nhefs_all.yrdth - 83) * 12 + nhefs_all.modth - 1\n",
    "nhefs_all.longevity.replace(np.NaN, 120, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhefs_all['survived'] = 1 - nhefs_all.death"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percent of survived at month 120 for untreated and treated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>qsmk</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>82.0%</td>\n",
       "      <td>76.2%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "qsmk          0      1\n",
       "survived  82.0%  76.2%"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nhefs_all.groupby('qsmk').agg(\n",
    "    {'survived': lambda col: '{:>0.1f}%'.format(100 * col.mean())}\n",
    ").T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log-rank test: \"a common statistical test to compare survival curves\", pg 211 margin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log-rank test p-value: 0.005\n"
     ]
    }
   ],
   "source": [
    "chisq, pvalue = sm.duration.survdiff(\n",
    "    nhefs_all.longevity,\n",
    "    nhefs_all.death,\n",
    "    nhefs_all.qsmk\n",
    ")\n",
    "\n",
    "print('log-rank test p-value: {:>0.3f}'.format(pvalue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Survival curve plot\n",
    "\n",
    "This plot is constructed by iterating through months and measuring the fraction of longevity values greater than that month. Those fraction values are the survival curve values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "longevity0 = nhefs_all.longevity.loc[nhefs_all.qsmk == 0]\n",
    "longevity1 = nhefs_all.longevity.loc[nhefs_all.qsmk == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# non-quitters\n",
    "max0 = int(longevity0[longevity0 < 120].max())  # max longevity for non-quitters\n",
    "surv_curve_0 = [\n",
    "    (longevity0 > i).sum() / longevity0.shape[0]  # fraction of longevities greather than current month i\n",
    "    for i in range(max0)\n",
    "]\n",
    "\n",
    "# quitters\n",
    "max1 = int(longevity1[longevity1 < 120].max())  # max longevity for quitters\n",
    "surv_curve_1 = [\n",
    "    (longevity1 > i).sum() / longevity1.shape[0]  # fraction of longevities greather than current month i\n",
    "    for i in range(max1)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(surv_curve_0)\n",
    "ax.plot(surv_curve_1)\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.73, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hazard at 120 months for treated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0/326 = 0.00%\n"
     ]
    }
   ],
   "source": [
    "numer = (longevity1 == 119).sum()\n",
    "denom = (longevity1 > 118).sum()\n",
    "print('{}/{} = {:>0.2f}%'.format(numer, denom, numer * 100.0 / denom))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hazard at 120 months for untreated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/986 = 0.10%\n"
     ]
    }
   ],
   "source": [
    "numer = (longevity0 == 119).sum()\n",
    "denom = (longevity0 > 118).sum()\n",
    "print('{}/{} = {:>0.2f}%'.format(numer, denom, numer * 100.0 / denom))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The description of the curves above is different from the book. The book describes calculating the hazard by \"dividing the number of cases during the interval $k$ by the number of individuals alive at the end of the interval $k - 1$\" and, from pg 212,\n",
    "\n",
    "$$\n",
    "\\text{Pr}[D_k = 0] = \\prod_{m=1}^k \\text{Pr}[D_m  =0 | D_{m-1} = 0]\n",
    "$$\n",
    "\n",
    "\"That is, the survival at $k$ equals the product of one minus the hazard at all previous times.\"\n",
    "\n",
    "I'll show that you get (roughly) the same values for the survival curve for `qsmk` = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hazard0 = [\n",
    "    (longevity0 == i).sum() / (longevity0 >= i - 1).sum()\n",
    "    for i in range(max0)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Survival curves will be calculated from hazard values multiple times throughout the notebook, so I'll make a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def survival_curve(hazard):\n",
    "    survival = [1 - hazard[0]]\n",
    "    for i in range(1, len(hazard)):\n",
    "        survival.append((1 - hazard[i]) * survival[i - 1])\n",
    "    return survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "survival0_v2 = survival_curve(hazard0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0, 1.0, 0.9991673605328892, 0.9983347210657785]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surv_curve_0[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0, 1.0, 0.9991673605328892, 0.9983354143542607]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survival0_v2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maximum difference between methods: 2.87e-04\n"
     ]
    }
   ],
   "source": [
    "max_diff = np.abs([a - b for a, b in zip(surv_curve_0, survival0_v2)]).max()\n",
    "print(\"maximum difference between methods: {:>0.2e}\".format(max_diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This difference is likely due to a propagation of rounding errors, and probably wouldn't be visible on the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 17.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Program 17.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the person-time format\n",
    "\n",
    "The following function will be used throughout this notebook to create the person-time format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def person_time_format(data):\n",
    "    # This works for the current data only; it requires the data\n",
    "    # have `longevity` and `death` columns.\n",
    "\n",
    "    # accumulate rows in a dict, then convert the dict to DataFrame;\n",
    "    # the dict will contain each column of the input data, plus\n",
    "    # `time`, which keeps track of month within individual, and\n",
    "    # `event`, a 0/1 indicator of death for each month\n",
    "    newrows = {name: [] for name in data.columns}\n",
    "    newrows.update({'time': [], 'event': []})\n",
    "    \n",
    "    for _, row in data.iterrows():\n",
    "        # n_mos: a zero for each month of `longevity`\n",
    "        # and then a zero/one to indicate death afterwards\n",
    "        n_mos = int(row.longevity + row.death)\n",
    "        for name in data.columns:\n",
    "            newrows[name].extend([row[name]] * n_mos)\n",
    "        newrows['time'].extend(range(n_mos))\n",
    "        newrows['event'].extend([0] * n_mos)\n",
    "        if row.death:\n",
    "            newrows['event'][-1] = 1\n",
    "\n",
    "    return pd.DataFrame(newrows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt_data = person_time_format(nhefs_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(176764, 84)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pt_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"An easy way to parametrically estimate the hazards is to fit a logistic regression model ...\", pg 213"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.013100\n",
      "         Iterations 11\n"
     ]
    }
   ],
   "source": [
    "model = sm.Logit.from_formula(\n",
    "    'event ~ qsmk + qsmk:time + qsmk:np.power(time, 2) + time + np.power(time, 2)',\n",
    "    data=pt_data\n",
    ")\n",
    "res = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>              <td>   -6.9956</td> <td>    0.231</td> <td>  -30.291</td> <td> 0.000</td> <td>   -7.448</td> <td>   -6.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk</th>                   <td>    0.3355</td> <td>    0.397</td> <td>    0.845</td> <td> 0.398</td> <td>   -0.443</td> <td>    1.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk:time</th>              <td>    0.0121</td> <td>    0.015</td> <td>    0.804</td> <td> 0.422</td> <td>   -0.017</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk:np.power(time, 2)</th> <td>   -0.0002</td> <td>    0.000</td> <td>   -1.293</td> <td> 0.196</td> <td>   -0.000</td> <td> 8.31e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                   <td>    0.0196</td> <td>    0.008</td> <td>    2.329</td> <td> 0.020</td> <td>    0.003</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th>      <td>   -0.0001</td> <td> 6.69e-05</td> <td>   -1.878</td> <td> 0.060</td> <td>   -0.000</td> <td> 5.47e-06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create predictions for each time point, for both `qsmk == 0` and `qsmk == 1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "A0_pred = res.predict(\n",
    "    pd.DataFrame({'time': list(range(120)), 'qsmk': [0] * 120})\n",
    ")\n",
    "A1_pred = res.predict(\n",
    "    pd.DataFrame({'time': list(range(120)), 'qsmk': [1] * 120})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just a quick look at the values of `A0_pred`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "A0_pred.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model-based survival curves, and recreate Figure 17.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `survival_curve` function below was defined in cell 22 above. For explanation, see the text between cells 20 and 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_surv_0 = survival_curve(A0_pred)\n",
    "model_surv_1 = survival_curve(A1_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ixuDaEYnclJXOTRPSGJqo+fAinY1CXaQpNadhy2991/ypo5A+2Yd75i2daq57MJxz5Bw7xdIcH/A7CyoAyBzYj0VZadw0IV27y4l0Egp1kcupq4Ftz8Lb/wNl+yA5w3fLT/oIxEbn0q2HSk+zLKeQZbkFbDp0AudgaGJPbprgW/DZIxI10E6kg1KoiwSjoR5yX4RVD0HhDug/DGZ+3e/vHtc90tW1m+KKal7PK2RpTgGr95ZSU99AUu94FmSmsWhiGtePTqZHNw20E+koFOoiLeEc7F4KK38ERzf5hWyuv9cvZBMf3fegzw20W5pTyJs7i6isrqN3fCxzx6eyKCudeeNStPGMSIQp1EVawzk48BasfNAvZNMrGWZ8Ga79fKddpa4lquvqWbOvlGWB+/AllTXEx8Zw/ZgkFmels2BCGsl9orcHQ6SjUqiLtNXhdT7c9y6HHv3hun+A6V+EngMiXVlYnNt4ZumOApbmFnCk7CwxBtkjElmUlc6irDSGDIjuXgyRjkKhLhIqx7b6cN/5MsT3hWlf8K333smRrixsnHPkHa/g1ZwClu4oYFehH0k/aXACiyemsygrnTGpfSJcpUj0UqiLhFrBDlj1IOS8CN16Qvbn/H33vmmRrizsDpacZmlOAa/mFLD18EkAxqT2YVFWGouzBjJxcD9NlRMJIYW6SHsp3uVb7jue8+vLX3M33PA16Dcw0pVFREF5FctzfcCv2++3jh3cvyc3ZaWxOCtdU+VEQkChLtLeSvfBqh/Du89ATBxM/bRfXz5hSKQri5gTp2t4LTBVbuWeEmrqGkjuE8/CCeksnpjOjFFJxMdF1yI/IuGgUBcJl7IDfhGbd37vn0/5FMz6hp/z3oVVVtexYlcRr+wo4M2dRZypqadvjzgWZKaxeGI6s7XpjEjQFOoi4XbysN/TfetvwTXA1Z/wq9R18m1fQ6Gqtp6395Twak4Br+UVcvJMLT27xTJ3XAqLJ6Yzb3wq/TQXXqRZHSbUzWwx8FMgFnjcOfefl7w+HHgSSAHKgE855/Ivd02FunRo5fl+T/fNT/ttX6+6E2Z/ExJHRbqyDuHcpjNLcwpYmlNAUUU18bEx3DAmicUT01mQmUaS5sKLXKRDhLqZxQK7gYVAPrARuNM5l9vonD8CLzvnnjazG4G7nXN3Xe66CnXpFE4dC4T7U1BfC5M/BrO/BUmjI11Zh9HQ4Nh65ASvbPcD7fJP+Lnw00cmnZ8ql57QI9JlikRcRwn1GcADzrlFgeffAXDO/bDROTnAIudcvvk5MOXOucsu3aVQl06logBWP+z3dK+vhkmBcE8eE+nKOpRzu8q9usMH/N6iSgCmDuvP4onpLM4ayLAkLXYjXVNHCfWPAIudc58PPL8LmO6c+0qjc34PrHfO/dTMbgP+BCQ750qbu65CXTqlikK/5evGJwLh/tFAuI+NdGUd0t6iCl7dUcArOwrIOXYKgAkD+3HzRD+Sfmxa3whXKBI+HSXUP4pvhTcO9WnOua82OmcQ8DNgJLASuB3Ics6VX3Kte4B7AIYNG3bNoUOHwvJ3EAm5yqIL4V5XBRNvh9nfhpSMSFfWYR0pOxMI+ONsCSx2MzqlNzdPHMjiielkDdJiNxLdOkqoX7H7/ZLz+wA7nXOXneirlrpEhcriQLg/DrVnfbjP+TakjIt0ZR1a4akqluX4Fvz6A36xmyEDerI4y7fgpw4bQIwWu5Eo01FCPQ4/UG4+cBQ/UO4TzrmcRuckA2XOuQYz+wFQ75z77uWuq1CXqHK6BNb8L2z4FdSeUbi3QNnpGl7LLeTVnALe3lNCTX0DKX27sygrjZsnDmTayES6xWqxG+n8OkSoBwp5H/AT/JS2J51zPzCz7wGbnHMvBe67/xBw+O73Lzvnqi93TYW6RKX3hPttvls+dXykK+sUKqpqeWNnEa/uKGDFrmLO1tbTv1c3FmSmcfPEdG4Yk0yPblrsRjqnDhPq7UGhLlHtdCmsDYR7zWmFeyucrannrd3FvLrjOK/nFVFRXUef7nHMG5/K4qx05o5LoXf3uEiXKRI0hbpIZ3e6FNb+DDb80od71od9t3xqZqQr61Rq6hpYva+EpTsKWJZbSNnpGrrHxTA7I4XFWX6xm4ReWs3uUi+88AK33XYbeXl5jB/f9v9DWV1dzac//Wk2b95MUlISzz77LCNGjGh7oV2EQl0kWijcQ6auvoGNB0/4bWN3FFBwqoq4GGPGaL/YzcIJaaT21WI3AB/72Mc4fvw48+fP54EHHmjz9X7+85+zbds2fvGLX/DMM8/wwgsv8Oyzz7a90C5CoS4Sbc6U+XBf/1gg3D/ku+XTJkS6sk6pocHxbv5JXs0pYOmOAg6WnsEMsocPYFFgJP2QAV1zsZvKykrGjRvHm2++yS233MLOnTvbfM1FixbxwAMPMGPGDOrq6khPT6e4uFhTEYN0uVDXjSSRzqhXIsz/Lsz4Cqx9xId7zgsw4UO+5Z6WFekKO5WYGGPKsAFMGTaA+xePZ1dhBa9s9+vRf/+veXz/r3lMHNzv/FS5MaldZ7GbF198kcWLF5ORkUFiYiJbtmxh6tSp7zlv1qxZVFRUvOf4gw8+yIIFCy46dvToUYYOHQpAXFwcCQkJlJaWkpyc3D5/iS5EoS7SmfVKhPn/CjO+DOt+Dut+AbkvQuYtMOf/QfrESFfY6ZgZ49P7MT69H19fmMHBktO+BZ9TwIPLdvPgst2MTul9frnaiYOje7GbJUuWcN999wFwxx13sGTJkiZDfdWqVUFfs6ke4mj+DMNJ3e8i0eRM2YVwr6mAzA8Gwn1SpCuLCgXlVSzL9ffgzy12M7h/T27KSmNRVjrXjkgkNooWuyktLWXIkCGkpqZiZtTX12NmHDp06D0h3JKWurrf20b31EW6mjNlsO5RWP8LqD4F4z/gw33g5EhXFjVOnK7htbxCXt1RwKq9JdTUNZDUO54FmWksnpjO9WOS6B7XuefCP/bYY2zZsoXHHnvs/LE5c+bw/e9/n1mzZrX6uo888gjbt28/P1Du+eef5w9/+EMoSu4SFOoiXdXZE77Vvu5RqC6Hce/399wHXR3pyqJKZXUdb+0q5tWcAt7cWURlYC783HEpLMpKZ974VPp0wrnwc+fO5f7772fx4sXnjz388MPk5eXx6KOPtvq6VVVV3HXXXWzdupXExESeeeYZRo0aFYqSuwSFukhXd/akH0y37hGoKoeMxT7cB18T6cqiTnVdPWv2lbJ0RwHLcwspPV1DfFwMM8cksygrjQWZaST16R7pMqUTU6iLiFdV7ue4r33Et+LHLIS598OQJv99kDaqb3BsPuTnwi/NKSD/xFliDLKHJ56/Dz80sWtOlZPWU6iLyMWqK3y4r/kZnC2D0TfCnPth2PRIVxa1nHPkHDvFstxCluUUsLPADyrLHNiPRVlp3DQhncyBfTVYTK5IoS4iTauuhE1PwOqH4UwJjJzjB9SNuCHSlUW9Q6WnWZZTyLLcAjYdOoFzMDSxJzdNSOemCWlkR9lIegkdhbqIXF7Nadj0pA/300UwfCbM+ZYPebUc211xRTWv5xWyLLfw/Laxib3jWZCZyk0T0pk5VrvKyQUKdREJTu1Z2Pw0rP4JVByHIdN8y33MfIV7mFRW17FiVxHLcwt5Y2cRFVV19OwWy+yMZBZOSGf++FQG9I6PdJkSQQp1EWmZ2ip45/9g1f/AqXwYNAVmfwvGvU/hHkY1dQ2s21/K8txClucWUnCqitgYI3v4AG7K8t30GmjX9SjURaR16mrg3SXw9kNw4iCkTYTZ/+iXoY1Rd3A4OefYfrSc5bmFLMspZFehH2g3Pr0vCyeksXBCGpMGJ2igXRfQ5lA3szjnXF3IKwsBhbpIGNTXwfY/wqofQ+keSM6AWf8IE2+H2M63qEo0OFx6hmW5fi78xoNlNDhI79eD+ZmpLJyQxozRnX9FO2laKEK9GHgaeMI5lxfi+tpEoS4SRg31fsOYlQ9CUS4MGAEzvw5X3QlxWlAlUspO1/DGziJeyy1k5Z5iztTU0zs+ljnjUliQmca8cboPH01CEepfAO4GpgMbgMeBZ51zlaEstDUU6iIR0NAAu1+BlT+CY1uh32C4/l6Y+mmI1z3eSKqqrWftvlKW5Rbyel4hRRXVfsGbEYkszExjfmYqo1L6RLpMaYOQ3VM3s0zgc8CngD7AH/Gt99WhKLQ1FOoiEeQc7HsdVv4YDq+BXskw40tw7eehR0Kkq+vyGhou3Id/La/w/II3o1J6syDTL1k7dVh/4mJjIlyptETIB8qZWSzwJeBHQDdgD/AT4JfOuYY21NpiCnWRDuLQGn/Pfe9r0D0Bpn0BrvsH6J0c6cokIP/EGV7LLeT1nUWs219Kbb2jf69uzBuXyvzMVGZnpNCvR7dIlylXEMqWejxwG761fiPwNvAEMAi4F1jlnLujzRW3gEJdpIM5thVWPQR5f4G4HnDNZ+H6r0DCkEhXJo1UVNWycncJr+cV8uauIk6cqSUuxpg2MpH5mWksyExleFLvSJcpTQjFPfWp+CC/E6gFfgP8yjm3p9E52fhQ7xmSqoOkUBfpoIp3wds/gW3PgsXAVR+HG74OyWMiXZlcor7BseXwCV7LK+SNvCL2FPnhUqMD3fQ3jk/lmuED1E3fQYQi1OuA5fgBcn9uanqbmfUGfuacu7uN9baIQl2kgzt52C8/u/W3UFcNE27xI+YHTYl0ZdKMw6VneH1nIa/nFbH+gO+m79cjjjnjUrlxfApzMlJJ1Gj6iAlFqA93zh0KeWUhoFAX6SQqi2Ddo7Dxcag+BaPm+nDX+vIdWmV1HW/vKea1vCJW7CqipLKGGIMpwwZw4/hU5o1L1e5yYRaKUN8PXOucK73keH9gi3NuVEgqbQWFukgnU1XuN49Z9yhUFvoW+w33QeYHtUpdB3duNP0bO4t4c1cR2/LLAb/ozbzxKcwbl8oNY5Lp3V0LErWnUIR6A5DunCu65HgacNg5F7FVJxTqIp1UbZVfgnbNw1C2HxJHw/Vf9QvZdOsR6eokCEWnqlixq5g3dhbx9t4SKqvriI+NYfqoROZkpDBvfCqjknurFR9irQ51M7st8ONzwN8B5Y1ejgXmA/Occ+NCVGuLKdRFOrmGej9SfvVP/Mj53qkw/Ytw7d9BzwGRrk6CVFPXwKZDZby5s4g3dxWzNzDYblhiL+aNS2HuuFSuG5VEz3j1xrRVW0L93JxzB1z6f7VqgYPAN51zL4egzlZRqItECefg4Ns+3Pe+Bt16+xXqZnwJ+g+LdHXSQkfKzrBidzErdhaxZl8pZ2vr6R4Xw/RRSczNSGHuuBRGqhXfKqHofj+Av6deEuri2kqhLhKFCnbAmv+FHc/5sM/6sO+aH3R1pCuTVqiqrWfDgTJW7Cpmxe4i9hefBnwrfk5GCnMyUpgxOkn34oOkrVdFpHMqz/cD6jY/DTUVMGKWX2N+zAKI0ZzpzupcK/6tXb4Vf6amnm6xxrUj/L342RkpjE/XiPrmtCrUzewbwM+dc1WBn5vlnHuo7WW2jkJdpAuoKofNT8G6X0DFMUgeBzO+DJM/rkF1nVx1XT2bD55gxe5iVu4uPr8+fWrf7swam8LsjGRmjU3RvPhGWhvqB4Bs51xp4OfmOE1pE5GwqK+FnBd813zBNr+BzLWf948+KZGuTkKgoLyKlbuLeWtPMW/vKaH8bC1mMGlwArPGJjN7bApThg0gPq7r9tSo+11EootzcGAlrPs57H4VYrvD5I/CdV+CtKxIVychUh+YF78y0IrfeuQk9Q2O3vGxzBidxKyxKcwcm9zlps0p1EUkepXsgbWPwLvPQN1Zv1LddV+CMQt13z3KlJ+tZe2+ElbtKeHtvSUcKj0DwKCEHucD/oYxyVHfVd+We+pB0T11EYm4M2Ww+dew4VdQcRySxsC0L8LVn4DufSJdnbSDQ6WnfcDvKWHNvhJOVfltSbIG9WPmmGRmjk3m2hGJ9OgWXXPj23JPPRi6py4iHUd9LeT+2Y+aP7oJuveDKXfBtM9DYsT+qZJ2Vt/g2JZ/ktV7fUt+y+ET1NY74uNiuGbYAGaOTeb60UlMGpzQ6XebU/e7iHRN+Zt8uOe+6Feuy1jkV6sbNU+byNcJFhkAACAASURBVES5MzV1bDhQxuq9Jby9t5S846cA6Ns9jumjkrhhTBI3jElmbGqfTnc/XqEuIl3bqeN+E5lNT8KZEkgaC9PugavugB79Il2dhEFJZTVr95Wyem8Jq/eVcKTsLADJfbpz/eikwCOZoYk9O3zIa566iAj4/dxzXoANv4SjmyG+jw/2az8PqZmRrk7C6EjZGdbsK2HNvlJW7y2lpLIagMH9ezJjdBIzRiUxY3QSg/r3jHCl76V56iIil8rfDBt/BTueh/pqv1rdtX8H4z8Asd0iXZ2EkXOOfcWVrNlXypq9paw7UMrJM7UADE/qxYxRSVwXeKQnRH6xI3W/i4g053QpbP0NbHwSyg9Dn3S/kcw1n4WEwZGuTiKgocGxs6CCtftLWbe/lPX7S8+PrB+Z3JvpIxO5blQS00clMjAh/C15hbqIyJU01MOe5bDpCf+nGWTcDNmfg9E3as57F1bf4Mg7fop1+0tZt7+MDQcuhPywxF5MH5nItEDQDxnQ/vfkQxLqZvYh4BvAhMChPOAh59wLIamylRTqIhJyJw7Cpl/D1v/zA+v6D4drPgNXfwr6pkW6OomwcyG//kAZ6/eXsuFg2fnu+kEJPZg2MpFpI5OYNnIAo1NCP7o+FFuvfhP4D+A3wNrA4RnAp4B/dc49GKJaW0yhLiLtpq4adr7sA/7gKoiJg3Hv813zo+ap9S6A767fXVTBhgNlgaAvOz/wLql3PNeOSOR/PzGFbiGaHx+KUD8OfNc596tLjn8B+J5zbmBIKm0FhbqIhEXJHtjyNLzzezhTCgnDYOpdcPUnde9dLuKc42DpGTYcKGX9gTKKK6r57d9ND9n1QxHqFcAU59zeS46PAbY65/qGpNJWUKiLSFida71v+Q3sXwEW4/d3n3IXZCyGuOhed1wi73KhHhfkNV4EPgL85yXHbwdeakNtIiKdS1x3mHi7f5QdgK2/9a33P9zlt4K96g6Y8inNe5eICHZDl77A14H1XLinfl3g8ZBz7nvtWeTlqKUuIhFXXwf7Xvet992vQkMdDJoKUz7pw7/ngEhXKFFEG7qIiIRLZTFs/wNs/R0U5fi93se/3+8WN2oexAbbQSrSNM1TFxEJN+fg+DvwzhIf8mdPQJ80mPRRuOpOSJ8Y6Qqlk1Koi4hEUl2N75bf9izsXgoNtZA2ESZ/HCZ9BPoNinSF0omEavGZDPxguWHARcM7nXOfa2uRraVQF5FO5XQp5DwP7y7xm8pgMHI2TP4YZH4QeiREukLp4EIxpe39wJ+ArcA1wEZgNNAdWOWcuyV05baMQl1EOq3Sfb71vu1Zv4pdbHe/5/ukj8LYm6Bb5DcPkY4nFKG+GXjOOffDwJz1q4BjwG+BtcFuvWpmi4GfArHA4865/7zk9WHA00D/wDn3O+f+drlrKtRFpNNzzrfat/3Bt+JPF0P3fn7HuIm3w6g52jlOzgtFqFcCk51z+82sDJjtnNthZpOAvzrnhgVxjVhgN7AQyMe39u90zuU2OueX+MVsHjWzCcDfnHMjLnddhbqIRJX6Oji4Erb/CfL+AtXl0DPRd81PvA2Gz9QI+i4uFIvPVADn+oGOA2OAHYHfD3YC5jRgr3Nuf6CoZ4BbgdxG5zigX+DnBHxvgIhI1xEb53eFG30jfOAh2Pua3/N9+3N+mdpeyT7gsz6kgJf3CPbbsB6YiQ/gvwI/NrOrgA9zYTGaKxkMHGn0PB+4dDHcB4BlZvZVoDewIMhri4hEn7jAHPfx74eaM7BnGeS+6O/Bb/61D/jx74cJt8BIddFL8KH+DaBP4OcH8CvM3Y7vTv9GM79zqab2nru07/9O4Cnn3I/NbAbwWzOb6JxruOhCZvcA9wAMG3bFnn8Rkc4vvpdvnWd9yAf83uWQ+2fY8Sffgu/RH8bd7Fvxo2+Ebj0jXbFEQNjmqQdC+gHn3KLA8+8AOOd+2OicHGCxc+5I4Pl+4DrnXFFz19U9dRHp0mqrYN8bPuB3vwJV5dCtF4yZ7wfajb0JeiVGukoJoVDcUz93oRuBCYGnuc65N1rw6xuBsWY2EjgK3AF84pJzDgPzgafMLBN/H7+4JTWKiHQp3XrA+Pf5R32t3/c972XY+Vc/0M5iYcQNMO79MG4xDBgR6YqlHQU7+n0k8DwwiQuD1wYB24Hbzw1+C+I67wN+gp+u9qRz7gdm9j1gk3PupcCI91/hu/od8G3n3LLLXVMtdRGRJjQ0wLGtfpvYnX+Fkl3+eOoE302fsRgGXwMxsZGtU1osFFPa3sAH8V3OucOBY+fmlDvn3I0hrLdFFOoiIkEo3eeXqt31ChxaA64eeiX57vmxC/19eO0m1ymEItTP4u9tv3vJ8avxi89EbESGQl1EpIXOnvRbxe5e6kfUnz3hu+mHToexC2DMQkifBNbU+GaJtFDcUz8MNBXcPbh4mpqIiHR0Pfv7leom3g4N9X41u91LYc9SeP17/tEnDUbP9wPuRs2D3kmRrlqCEGyofxN42MzuxQ94A7gWf3/8m+1RmIiIhEFMLAyd5h/z/xUqCvxo+j3LYdff4N3fAwaDrvbhPnqeb9HHdY905dKEZrvfA2u8N36xB/6++rk54zFAPVDlnOtHhKj7XUSknTTUw7F3fFf93tchf6O/Fx/XE4Zf79ekHzUX0iZBTEykq+0yWtv9/pV2qkdERDqDmFgYco1/zPk2VJ2CQ6th35uwfwUs/64/r2einzY3cg6MmAUp43Q/PkKaDXXn3NPhLERERDq4Hv38dLhxN/vnp47Dgbdg/1uB+fF/8cd7p8CImTD8BoV8mAW9+IyZdQc+iV98xgE5wBLnXHU71SYiIh1Zv4Fw1R3+4ZzfE/7ASt+aP7AKcl7w5/VKgmEzfMgPn+G767URTbsIdkrbBOBV/A5q2wOHJwHl+GVd89qtwivQPXURkQ7IOThxAA6uhsNrfdCfOOhf69Ybhl4LQ6+DYdNhcLbvBZCghGKe+nLgDH7xmVOBY/2A/wO6n1vPPRIU6iIinUT5UTiyDg6thcProHAH4MBiIDXLB/2QwEj8xFHqsm9GKEL9DHCtcy7nkuOTgHXOud4hqbQVFOoiIp1UVTnkb4IjG+DIej9fvvqUf61nol/Gdki2b8kPnqqNaQJCsfhMFdC/ieMJgddERERapkeCX9xmzHz/vKEeindB/gY/fS5/M+x9jfOzqweMgEFTfcAPmgIDr4LufSNVfYcUbKj/BfiVmX0BWBc4NgN4DHipPQoTEZEuJiYW0ib4xzWf9ceqTvmNaY5tgaNbfKs+5/nALxgkj/XhPvBq/2f6JL9iXhcVbKh/Db95yyr8gjPgF595CbivHeoSERHxA+hGzfGPcyqL/KI4x7b6x6E1sP2PF17vPwzSJwceEyFtoj/WBe7RBxXqzrmTwK1mNgbIBAy/n/re9ixORETkPfqkQsZN/nFOZTEcfxcKtvnH8W1+y9lzXffdEwK9AFl++9m0LEgZH3Wt+iuGupl1w2/aMj8wUE5BLiIiHUufFL/D3NgFF45VV0JRHhRuh4LtUJgL7z4LNRUXzuk3GFIzfcCnjPc/J2d02il2Vwx151ytmdVy8TrwIiIiHVv3PoH58NdeOOYcnDwMxTuhKNeHflGuXyynvtFaan0HQUoGJI/z9+2Tx/qw7zuwQ3fjB3tP/X+B75jZ3c65uvYsSEREpN2YwYDh/pHRaImVhnq/OE5RHpTsguLd/s93fgc1lRfO69Ybkkb7kE8c7X8+92cHmHIXbKjPAuYAR81sB3C68YvOuVtCXZiIiEjYxMT6YE4aDXzgwnHnoOI4lOyGkj1Qutc/8jf5ZXBdw4VzeyTAgJF+4ZzEkX4K3oDAnwlDwtLCDzbUS4A/tWchIiIiHY4Z9BvkH6PmXvxaXTWcOARl+6B0n18Wt2y/n36X+2e/TS341v0/HQ1LucGOfr+7vQsRERHpVOK6+/vuKRnvfa2+Dk7l+y79syfCdh++RdvkmNlo/JQ28FPa9oe+JBERkU4uNi7Q/T4irG8bVKibWRLwBHAL0HDhsL0MfM45V9pO9YmIiEiQYoI873FgDH7AXI/AYzYwEvhV+5QmIiIiLRFs9/si/OIzaxsdW21mXwReC31ZIiIi0lLBttSLuWQaW8AZQF3vIiIiHUCwof494CdmNvjcgcDPPw68JiIiIhEWbPf7fcAI4KCZnZtsNxi/l3qqmd177kTn3OSQVigiIiJBCTbUn2vXKkRERKTNgl185t/buxARERFpm2DvqYuIiEgHp1AXERGJEgp1ERG5rBdeeAEzY+fOnSG53sqVK5k6dSpxcXE895yGbIWSQl1ERC5ryZIlzJw5k2eeeSYk1xs2bBhPPfUUn/jEJ0JyPblAoS4iIs2qrKxk9erVPPHEEyEL9REjRjB58mRiYhRBodbs6Hcz+0awF3HOPRSackREpCN58cUXWbx4MRkZGSQmJrJlyxamTp36nvNmzZpFRUXFe44/+OCDLFiwIBylCpef0vbVIK/hAIW6iEgUWrJkCffddx8Ad9xxB0uWLGky1FetWhXu0qQJzYa6c25kOAsREZGOpbS0lDfeeIMdO3ZgZtTX12Nm/Pd//zdmdtG5aql3DMGuKCciIl3Mc889x6c//Wkee+yx88fmzJnD22+/zaxZsy46Vy31jiHoUQpmlmhmnzCz+83su40f7VmgiIhExpIlS/jwhz980bHbb7+d3//+92267saNGxkyZAh//OMf+eIXv0hWVlabricXmHPuyieZXQf8FagGUoCjwMDA84OR3MQlOzvbbdq0KVJvLyIiElZmttk5l93Ua8G21H8E/I4LO7PdCAwDNgH/FYoiRUREpG2CDfXJwM+cb9bXA92dc4XA/wMeaKfaREREpAWCDfWaRj8XAsMDP1cCg0JakYiIiLRKsKPftwDXAruBFcD3zSwN+BSwrX1KExERkZYItqX+z8CxwM//AhQD/wsMAO5ph7pERESkhYJqqTvnNjX6uRi4ud0qEhERkVYJqqVuZv9jZu9dF1BEREQ6jGC736cDm8wsz8z+ycxGtF9JIiIi0hpBhbpz7npgNH6u+qeAfWa2ysy+aGYD2rNAERERCU7Qy8Q65w44577vnJuAHwm/HvhXLgygExERkQhq7Q713YDuQDx+MRoRERGJsJZs6JJhZv9uZnuAt4FxwD8Cae1VnIiIiAQvqCltZrYJmAK8CzwK/N45V9CehYmIiEjLBLui3DLgLudcXnsWIyIiIq0X7Oj3fwpFoJvZYjPbZWZ7zez+Jl7/HzN7J/DYbWYn2/qeIiIiXUWzLXUzexj4jnPudODnZjnn7r3SG5lZLPAIsBDIBzaa2UvOudxG1/l6o/O/iu/yFxERkSBcrvt9En6U+7mfm+OCfK9pwF7n3H4AM3sGuBXIbeb8O4F/C/LaIiIiXV6zoe6cm9fUz20wGDjS6Hk+fqW69zCz4cBI4I1mXr+HwEYyw4YNC0FpIiIinV+wa7/fGug+bwtr4lhzrfw7gOecc03OgXfO/dI5l+2cy05JSWljWSIiItEh2HnqS4BCM3vUzK5v5XvlA0MbPR9C86vR3RF4TxEREQlSsKGeBnwLGAOsNLP9Zvb/mdm4FrzXRmCsmY00s3h8cL906UmBaw4A1rbg2iIiIl1esFPaKpxzv3bOLcS3tn+G31M918w2BHmNOuArwFIgD/iDcy7HzL5nZrc0OvVO4BnnXLAD8ERERASw1mRnoKX9QeBfgMnOubbeb2+17Oxst2nTpki9vYiISFiZ2WbnXHZTr7VoQxczm2dmjwOFwOPAVmBB20sUERGRtgp27fcHgY8Dqfju8y8Cf3bOVbdjbSIiItICwa79fj3wQ/y97rJ2rEdERERa6Yrd72bWDb9ozFIFuoiISMd1xVB3ztUCNxH8crAiIiISAcEOlHseuK09CxEREZG2Cfae+mHgX8xsFrAJON34RefcQ6EuTERERFom2FD/LHACmBx4NOYAhbqIiEiEBRXqzrmR7V2IiIiItE2LFp8RERGRjivYxWcevtzrzrl7Q1OOiIiItFaw99QnXfK8GzA+8PtbQlqRiIiItEqw99TnXXrMzHoATwCrQl2UiIiItFyr76k756qAHwD/HLpyREREpLXaOlAuBegTikJERESkbYIdKPeNSw8BA4FPAn8LdVEiIiLScsEOlPvqJc8bgGLg1/jd20RERCTCtPiMiIhIlGjVPXUzizMz3UsXERHpQC4b6mY238w+dsmx+4FK4KSZvWpm/duzQBEREQnOlVrq9wNDzj0xs2nAfwC/Bb4NXIWmtImIiHQIVwr1ScBbjZ5/FFjjnPtCYLvVe4Fb2qs4ERERCd6VQr0/UNTo+Q3Aq42ebwQGh7ooERERabkrhfpxYDSAmXUHpgBrG73eF6hun9JERESkJa4U6q8A/21mNwL/BZzm4rXeJwN726k2ERERaYErzVP/LvA88Bp+xPtnnHM1jV7/HLC8nWoTERGRFrhsqDvnSoDZZpYAVDrn6i855aP4sBcREZEIC3ZFufJmjpeFthwRERFprbbu0iYiIiIdhEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSoQ11M1ssZntMrO9ZnZ/M+d8zMxyzSzHzH4fzvpEREQ6s7hwvZGZxQKPAAuBfGCjmb3knMttdM5Y4DvADc65E2aWGq76REREOrtwttSnAXudc/udczXAM8Ctl5zzBeAR59wJAOdcURjrExER6dTCGeqDgSONnucHjjWWAWSY2WozW2dmi5u6kJndY2abzGxTcXFxO5UrIiLSuYQz1K2JY+6S53HAWGAucCfwuJn1f88vOfdL51y2cy47JSUl5IWKiIh0RuEM9XxgaKPnQ4BjTZzzZ+dcrXPuALALH/IiIiJyBeEM9Y3AWDMbaWbxwB3AS5ec8yIwD8DMkvHd8fvDWKOIiEinFbZQd87VAV8BlgJ5wB+cczlm9j0zuyVw2lKg1MxygTeBbznnSsNVo4iISGdmzl16W7tzyc7Odps2bYp0GSIiImFhZpudc9lNvaYV5URERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREooRCXUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURUREokRYQ93MFpvZLjPba2b3N/H6Z82s2MzeCTw+H876REREOrO4cL2RmcUCjwALgXxgo5m95JzLveTUZ51zXwlXXSIiItEinC31acBe59x+51wN8AxwaxjfX0REJKqFM9QHA0caPc8PHLvU7Wa2zcyeM7Oh4SlNRESk8wtb9ztgTRxzlzz/C7DEOVdtZn8PPA3c+J4Lmd0D3BN4Wmlmu0JYZzJQEsLrRQN9JhfT53ExfR4X0+dxMX0e79XWz2R4cy+Yc5fmavswsxnAA865RYHn3wFwzv2wmfNjgTLnXEJYCrzwvpucc9nhfM+OTp/JxfR5XEyfx8X0eVxMn8d7tednEs7u943AWDMbaWbxwB3AS41PMLOBjZ7eAuSFsT4REZFOLWzd7865OjP7CrAUiAWedM7lmNn3gE3OuZeAe83sFqAOKAM+G676REREOrtw3lPHOfc34G+XHPtuo5+/A3wnnDU14ZcRfv+OSJ/JxfR5XEyfx8X0eVxMn8d7tdtnErZ76iIiItK+tEysiIhIlFCoN3KlZWyjnZkNNbM3zSzPzHLM7GuB44lmttzM9gT+HBDpWsPJzGLNbKuZvRx4PtLM1gc+j2cDAz+7BDPrH1hDYmfgezJD3w/7euC/lx1mtsTMenSl74iZPWlmRWa2o9GxJr8T5j0c+Dd2m5lNjVzl7aOZz+NHgf9mtpnZC2bWv9Fr3wl8HrvMbFFb31+hHtBoGdubgQnAnWY2IbJVhV0d8E3nXCZwHfDlwGdwP/C6c24s8HrgeVfyNS6eifFfwP8EPo8TwN9FpKrI+CnwqnNuPHAV/nPpst8PMxsM3AtkO+cm4gcB30HX+o48BSy+5Fhz34mbgbGBxz3Ao2GqMZye4r2fx3JgonNuMrCbwNixwL+vdwBZgd/5eSCLWk2hfkGXX8bWOXfcObcl8HMF/h/swfjP4enAaU8DH4pMheFnZkOA9wOPB54bfkGk5wKndJnPw8z6AbOBJwCcczXOuZN04e9HQBzQ08zigF7AcbrQd8Q5txI/W6mx5r4TtwK/cd46oP8lU5k7vaY+D+fcMudcXeDpOmBI4OdbgWecc9XOuQPAXnwWtZpC/YJgl7HtEsxsBDAFWA+kOeeOgw9+IDVylYXdT4BvAw2B50nAyUb/gXal78kooBj4deB2xONm1psu/P1wzh0FHgQO48O8HNhM1/2OnNPcd0L/zsLngFcCP4f881CoXxDMMrZdgpn1Af4E3OecOxXpeiLFzD4AFDnnNjc+3MSpXeV7EgdMBR51zk0BTtOFutqbErhXfCswEhgE9MZ3MV+qq3xHrqQr//eDmf0z/jbn784dauK0Nn0eCvUL8oHGG8gMAY5FqJaIMbNu+ED/nXPu+cDhwnNdZIE/iyJVX5jdANxiZgfxt2NuxLfc+we6WqFrfU/ygXzn3PrA8+fwId9Vvx8AC4ADzrli51wt8DxwPV33O3JOc9+JLvvvrJl9BvgA8El3YS55yD8PhfoFV1zGNtoF7hc/AeQ55x5q9NJLwGcCP38G+HO4a4sE59x3nHNDnHMj8N+HN5xznwTeBD4SOK0rfR4FwBEzGxc4NB/IpYt+PwIOA9eZWa/Afz/nPpMu+R1ppLnvxEvApwOj4K8Dys9100czM1sM/D/gFufcmUYvvQTcYWbdzWwkfgDhhja9lxafucDM3odviZ1bxvYHES4prMxsJrAK2M6Fe8j/hL+v/gdgGP4fsY865y4dGBPVzGwu8I/OuQ+Y2Sh8yz0R2Ap8yjlXHcn6wsXMrsYPGowH9gN34xsHXfb7YWb/Dnwc3626Ffg8/r5ol/iOmNkSYC5+57FC4N+AF2niOxH4Pz4/w4/0PgPc7ZzbFIm620szn8d3gO5AaeC0dc65vw+c/8/4++x1+Fuer1x6zRa9v0JdREQkOqj7XUREJEoo1EVERKKEQl1ERCRKKNRFRESihEJdREQkSijURTohM1thZj+LwPveY2aHzazBzB5o5px0M1tmZqfNLKjpNWb2WTOrbO65iARHoS4SJDN7ysycmT3exGv/HXjt5RC/59zAdZNDed1W1jIAv5Phj/DzsB9s5tR/xC+ZejUQVZt1iHR0CnWRljkCfDywkQkAgeVA78IvshHNhuPXf385sKNfcy3pMcBm59yewCp0IhImCnWRltkG7AE+1ujY+4EqYEXjE80sxsz+1cyOmFm1mW03s1sbvT4i0Aq/3cyWm9kZM8s1s4XnXscvNwpQHDj3qUZvEWNm/2FmJWZWZGYPmllMo+vfZmbbzOysmZWZ2VtmltbcX8zMhpnZC2ZWEXg8H9h6FjP7LH5lNID9gVpGNHGNg/gNTj7duN7LXTtYZvZFM9trZjWBP7/Q6LX/MrNXGj3/QuD9P97o2OrA6l3NXf+pS3tazOwBM9tx6Tlm9i9mVmhmlWb2azPr2ZK/i0h7UaiLtNwT+GUdz/kc8Gveu7vS14Bv4dd8ngS8ADwfWGq1sR8ADwNX4fcgeMb8TnlHgNsD52Thu7K/1uj3PolfWvJ64CvAffjlSjGzdPwypU8Dmfh90H/b3F8osHzni0AafuOaefgu9BcDrz2LX9oT/H7PA7l4y8hzrgVewy8ROhD4WhDXviIz+zB+edGfABOBnwI/N7MPBk5ZAcy0C5uozAVKAu+FmfUK1LYimPe7gjn4/63m4//3uQn4r/+/vXsJrauI4zj+/WO7shSEKtKiBsVFBQUfi0ojZlHd1EWtS11IXYiCihsXCtXWhdLiRsGNUrKoKxELDUJ9lKt9BAoRG6IiPlCwQVHwdUMxsf5c/OfW0+O9ufekCYHr7wNDzpkzZ86cmxvmzMyZzDLka3bxJDk4OAwQgHFgArgMOEsuvnAl8Cf5P67Hya7pTvozwO5aHi3gYNkeIR8EHq4c31TiRsv+WNnf0CWfyVrce8DrZfuWct41A97bXcA5YKQSdy25BsC2sn9byXOkT14TwHjDvB8E2pXj9f0T5HoM9d/H8bK9DlgAbi/735PLwn5RKcMcsLbf77cW9xwwU0vzK7CuEvdA+Q5cutrfUQcHt9TNGpL0C9nq3kWuQNWSdMF4ekSsJ1ujJ2qnHwduqMVNV7Y7yy5eMUBRpmv7s5XzTpMt5pmIeCsiHomIyxfJazMwK+nbToSkb0qe9fI2tRx5b2aRz1I5vv8xMBYR1wPryZb91RGxkXw4OilpoQwFtCvh6Yb3M60L3yeYJBe4ua5hPmbLbk3/JGbWxQGya7sN7F4kXbcpXfW4hfMHJJUe6UEeuBdq++qcJ+lcRNwNrDrxMgAAAiNJREFUbCG7hx8CXoiIOyWd7pJX9Chrt/I2tVx59/ssW2R3+8/AMUntiDhFVuhjwDsl3Sz5Zn5HZ0W5v0tZq9Y2KJ/ZqnNL3WxpPgDmyeUVD9UPSvqdrDxGa4dGyfW2BzVffl7StIBKk5L2kOPJs5Qx9y4+AzZVX36LXGJ2Y8PyrlTen9P/s2wBW8mu9lYlbjuV8XRJf0n6qhI6lfpP/HcKXv39B4Abq7MfyAeneeDrAe/FbMW4pW62BKVFfRO5fHGvdbL3A3sj4ktgihx7vQO4tcGlviNbo9sj4jBwVr2nkp0XEVuAbcARck3nm4Gr6F2Jvk922b8REY+TLdZXyC7tow3Ku1J57wfejIgp4F3ypb37gZ2VNMfIbvCdwL4S1wKeIce8T/W5xlHgqYjYBXxU8tlKjs9XrQEORMRe8sHkReA1SXMD3ovZinFL3WyJJP1RWuS9vExWRvuAGeBe4D5JnzS4xhngWfIN+R/JceJB/EZWSBPkFLyXgOclHexxHQE7yNZqi5xK9wOwoxxbsuXIW9Ih4DHgSfLB5AngUUmHK2na5MPTHP9Ov5skZwiclFQfrqhf4wiwh/ysp8gXGV/tkvRD4NNyH29THgYGuQ+zlRYX+fdqZva/Uebdb5B0z2qXxawbt9TNzMyGhCt1MzOzIeHudzMzsyHhlrqZmdmQcKVuZmY2JFypm5mZDQlX6mZmZkPClbqZmdmQcKVuZmY2JP4B6pPNoYr26B4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(model_surv_0)\n",
    "ax.plot(model_surv_1)\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.73, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"These curves are a smooth version of those in Figure 17.1\", pg 213\n",
    "\n",
    "Plot the new parametric version and the previous nonparametric version together, just to see how well they line up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(surv_curve_0)\n",
    "ax.plot(surv_curve_1)\n",
    "\n",
    "ax.plot(model_surv_0)\n",
    "ax.plot(model_surv_1)\n",
    "\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.73, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 17.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Program 17.3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"The estimation of IP weighted survival curves has two steps\"\n",
    "\n",
    "\"First, we estimate the stabilized IP weights $SW^A$\", pg 217"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll once again borrow a function from Chapter 12 to create IP weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logit_ip_weights(y, X):\n",
    "    \"\"\"\n",
    "    Create IP weights from logistic regression\n",
    "    \n",
    "    Parameters\n",
    "    ----------\n",
    "    y : Pandas Series\n",
    "    X : Pandas DataFrame\n",
    "    \n",
    "    Returns\n",
    "    -------\n",
    "    Numpy array of IP weights\n",
    "    \n",
    "    \"\"\"\n",
    "    model = sm.Logit(y, X)\n",
    "    res = model.fit()\n",
    "    weights = np.zeros(X.shape[0])\n",
    "    weights[y == 1] = res.predict(X.loc[y == 1])\n",
    "    weights[y == 0] = 1 - res.predict(X.loc[y == 0])\n",
    "    return weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = nhefs_all[[\n",
    "    'one', 'sex', 'race', 'edu_2', 'edu_3', 'edu_4', 'edu_5', \n",
    "    'exercise_1', 'exercise_2', 'active_1', 'active_2',\n",
    "    'age', 'age^2', 'wt71', 'wt71^2',\n",
    "    'smokeintensity', 'smokeintensity^2', 'smokeyrs', 'smokeyrs^2'\n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.542264\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "ip_denom = logit_ip_weights(nhefs_all.qsmk, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr_qsmk = nhefs_all.qsmk.mean()\n",
    "\n",
    "ip_numer = np.zeros(ip_denom.shape[0])\n",
    "ip_numer[nhefs_all.qsmk == 0] = 1 - pr_qsmk\n",
    "ip_numer[nhefs_all.qsmk == 1] = pr_qsmk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ip_weights = ip_numer / ip_denom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stabilized weights\n",
      " min   mean    max\n",
      "------------------\n",
      "0.33   1.00   4.21\n"
     ]
    }
   ],
   "source": [
    "print('Stabilized weights')\n",
    "print(' min   mean    max')\n",
    "print('------------------')\n",
    "print('{:>04.2f}   {:>04.2f}   {:>04.2f}'.format(\n",
    "    ip_weights.min(),\n",
    "    ip_weights.mean(),\n",
    "    ip_weights.max()\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Second, using the person-time format, we fit a harzards model like the one described in Section 17.2, except that individuals are weighted by their estimated $SW^A$\", pg 217\n",
    "\n",
    "So, recreate the person-time dataset, but with weights, and fit the model. We can create the person-time format by adding the IP weights to the dataset, and using the function `person_time_format` from above. Since that function expands every column into the person-time format, it'll handle the weights too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "nhefs_all['weight'] = ip_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pt_data = person_time_format(nhefs_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>event</td>      <th>  No. Observations:  </th>  <td>176764</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th> <td>176922.33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>     5</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -2313.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 22 Jan 2020</td> <th>  Deviance:          </th> <td>  4626.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:46:47</td>     <th>  Pearson chi2:      </th> <td>1.76e+05</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>10</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>              <td>   -6.8970</td> <td>    0.221</td> <td>  -31.241</td> <td> 0.000</td> <td>   -7.330</td> <td>   -6.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk</th>                   <td>   -0.1794</td> <td>    0.440</td> <td>   -0.408</td> <td> 0.683</td> <td>   -1.042</td> <td>    0.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk:time</th>              <td>    0.0189</td> <td>    0.016</td> <td>    1.155</td> <td> 0.248</td> <td>   -0.013</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk:np.power(time, 2)</th> <td>   -0.0002</td> <td>    0.000</td> <td>   -1.556</td> <td> 0.120</td> <td>   -0.000</td> <td> 5.47e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                   <td>    0.0189</td> <td>    0.008</td> <td>    2.345</td> <td> 0.019</td> <td>    0.003</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(time, 2)</th>      <td>   -0.0001</td> <td>  6.4e-05</td> <td>   -1.846</td> <td> 0.065</td> <td>   -0.000</td> <td> 7.31e-06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                  event   No. Observations:               176764\n",
       "Model:                            GLM   Df Residuals:                176922.33\n",
       "Model Family:                Binomial   Df Model:                            5\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2313.1\n",
       "Date:                Wed, 22 Jan 2020   Deviance:                       4626.2\n",
       "Time:                        21:46:47   Pearson chi2:                 1.76e+05\n",
       "No. Iterations:                    10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "Intercept                 -6.8970      0.221    -31.241      0.000      -7.330      -6.464\n",
       "qsmk                      -0.1794      0.440     -0.408      0.683      -1.042       0.683\n",
       "qsmk:time                  0.0189      0.016      1.155      0.248      -0.013       0.051\n",
       "qsmk:np.power(time, 2)    -0.0002      0.000     -1.556      0.120      -0.000    5.47e-05\n",
       "time                       0.0189      0.008      2.345      0.019       0.003       0.035\n",
       "np.power(time, 2)         -0.0001    6.4e-05     -1.846      0.065      -0.000    7.31e-06\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = sm.GLM.from_formula(\n",
    "    'event ~ qsmk + qsmk:time + qsmk:np.power(time, 2) + time + np.power(time, 2)',\n",
    "    freq_weights=pt_data.weight,\n",
    "    family=sm.families.Binomial(),\n",
    "    data=pt_data\n",
    ")\n",
    "res = model.fit()\n",
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the curves is the same as last plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "A0_pred = res.predict(\n",
    "    pd.DataFrame({'time': list(range(120)), 'qsmk': [0] * 120})\n",
    ")\n",
    "A1_pred = res.predict(\n",
    "    pd.DataFrame({'time': list(range(120)), 'qsmk': [1] * 120})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `survival_curve` function below was defined in cell 22 above. For explanation, see the text between cells 20 and 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "surv_ip_0 = survival_curve(A0_pred)\n",
    "surv_ip_1 = survival_curve(A1_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(surv_ip_0)\n",
    "ax.plot(surv_ip_1)\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.76, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the curves on top of the curves from the previous model, to see how they've changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L8+NuhZRbwW8oAJUNrXyaVc6mrDK251bQYrHj4+7M3JgQFiWEMjs6GE9XZ7JK6kgc6gfALz/K4L19hRz41ULcXZwoqG4iyNsND1cnx34HIcRlTYK6GFiaa2DHMzpLPjReZ7nnfwWJ159Zl767zI16ef7Af/TyvGGCMfN0hn7MVboYDtBstvHFkUo2ZpayJbuc6kbzqX34RQmhLIwLJcTXnfK6FnLK6pk1NhiA217cSU2ThbU/nAVAfYsFH3c5Gy+E6B4J6mJg2/YX2PYneDAdfMN75jOqj+m994NvQF0ReAToZjbjbz8jQ99mV+w/cZKNmaVsyCw7dVxu/HB/FieEsSg+lNHB3gB8lVdJU6uNBfGhKKW44o+fsighjEdX6u5zUqNeCNEVEtTFwKYUlB+CUB0M+fh74B0G83/p+M+y2+DoZ7D/dcheC3YLDJ0IE+7QKwVuHd3ilFIcLmtgY2YpGw+VkV5UC8DYEG8WJ4SxOCGMxKG+GIaB2WrntR3HiQrxZk5MCHUtFhY8vo1fLo9nRUqE47+HEGLQkqAuBg+l9FE479COoF6eDSGxjv+sxip99n3/a7q4jYsXJF4LE+6CyEnnZOkX1TSzqW0Gv/t4NTa7Yqi/B4sSQlmSEMakkQGnWsQW1zTzlw053DF9BOOHDyGrpI7frz3EoysSGBvqc57BCCGEJkFdDF5F++GFuXDdi5B8Y898hlJQtA/2vwrp74OlEULiYcKdkHzTebP0qxvNbMkqY0NmKZ/nVmK22gn0cmVRQiiLE8KYMSYIV2fTqfd/daSSX6/K5L/3TyPYx43tuRXsyKvie3Oj5LicEOIMEtTF4NVarxPdxt0G7r5w/Es4eQySvwFOPZCU1loPGe/DvleheD84uemjcRPv0n3lz7NH3tBqZWtOORsyy/g0q4xGsw0fd2cWxOkAPzs6+JwM+Wc+O8IrXx1n5yPzcTIZfJpdhs0OC+NDHf+dhBADigR1cflY9UM4shl+eFBnyrdXsOsJJWl69p72DrTWQVAMTLxTV6+7wBn7FouNL49U8klGKZuyyqhpsuDh4sTc2GCWJIYzLzYE77aZeavVhpuzDva3vrCTJrONj753BQA78qoYE+JFiI97z3w3IUS/JUFdXD6U0tnrfpG6icwrV0HSDTD5vp77THMjZH4I+16Bwj2dmr0DWGx2dh+rZn1GCRsyy6iob8XV2cSVY4NYmhjOgrhQ/DxdTr23vL6Vof4eWG12Jj22mbkxIfztG+MAOFHVxLAAD8mmF+IyIEFdXJ5aanVSXexyvfdtNeuAHzCq5z6zNEMH97S39ew9OBYm3q2Px3n4X/CvtR+VW5dewoaMUoprW3A2GVwRFcRVSWEsjA8jwEuf0VdKkV1aj7PJYGyoD+X1LUx5bAu/XB7PvTNHYbcrFJxKyhNCDC4S1IUAOPAGrPo+fGsbhCf37GeZGyHjA9j7st57d/bQR+Im3QNDJ1x0S0ApRWphLevTS1iXUUJBdTNOJoNpowO4KimcxQlhBHm7nXp/XYuFVQeLuSIqiFFBXuw5Xs0Dr+/j5bsmM27YhX+REEIMTBLUhQCoL9X73zN+oINq1hrwDIQR03v2c4sP6uCe/p7OnA9L1sE96UZw877oX1VKkVlcx/qMEtall3KsshGTAVNHBXJVUhiLE8PO2VfPLK7lhc+P8rtrEvFxd+Hjg0WsSSvh8ZtS8JWKdkIMeBLUhTibUvDsTB3U71zVO5/ZUgfp78Def0NZBrj6QMo3dIBvL6xzEe3L7uvTS1ibXkJeRSOGAVNG6hn80sQwQnzPTZx7c/cJ3t9XyLvfno5hGLy95wStVjt3TB/ZA19SCNHTJKgLcT7mRmiqAv/hut78W7fB/F/B8Kk9+7lK6YS6PS/pBDtbKwybppP54leeqjl/8VvoanZr00tYl17CkfIGDAMmjwxg2UUCPMC3XttLk9nGf+7T33N1ajExYT5ES9EbIQYECepCfJ2SNHjvbrjhZQhP0efRnd175qz76Zqqdb35PS/p8/WeQbqhzKS7YcjITt8mt6z+VIA/XNYR4Jcnh7PkPEv0zWYbHq5OmK12JvxuE9eMj+D31yQBcLCghqShfpJoJ0Q/JUFdiM6w28HUVuVtwy8gZx18Zwe49MJZcLsdjm3VwT1nnZ7Nj12oZ+9RC8DU+fat5wvwU0cFsCw5giUJYQT7nLkSUFHfisVmJ8Lfg+OVjcz561Z+szKBO2eMxGqzY7Ur3F2kfawQ/YUEdSEuVe5mKDkIV/6Pvj6yRZ85d/Xs+c+uLdQV6/a/Cg1lentg0j26Y5xX0CXd6nBZPWvTSliTVkxehU6ymzY6kOXJESxOCCXQ+8wA32S28ll2BZNGDiHU153Pcsr5/hv7efuB6af6xgsh+pYEdSG6o74U/pYA078HC3/be59rs0D2Gj17P74dnFx1j/nJ90Hk5EuqlNe+B78mrZg1aSUcq2zEyWQwY0wgy5P1MTl/z3N71WeX1vHGzhP83/I43Jyd+M/OfDZnlfGv2yaeU9pWCNE7JKgL0V0ndoL/CN3PvewQHPpYB3l33975/PJs2PsSHHwTzPX6WNzk+3S1PFevS7qVUoqskvpTAf5EdRPOJoOZY4NYnhzBooTQCx59e2NXPluyynn5rskAvPLlMVycTdw2dUS3v6IQonMkqAvhSF89Bdv+DD9K1TXee7K+/NlaG/SxuN0vQnkmuPnB+Ntg0r0QFHXJt1NKkVFUx5r0YtakllBU04yrk4kro4NZkRLO/LjQU7Xoz+fOl3fj4eLEs7dPBODjg0UkR/ozKujSftEQQnSeBHUhHK2xCrwC9c9vfxMixsOsh3rv85XSqwd7XtSrBnYLjJ6rZ+/RS8Dp0tu1KqU4WFDDmrQS1qaVUFrXgpuziXmxIaxIiWBuTMh5l9xbLDbcXZxobLUy/rebuHvmSB5ZGneqcE5ChK/UpBfCgSSoC9FTrGZdejYsSVeqUwoaK8A7pPfGUF8GB17TRW3qisA3Uh+Jm3AneAd36ZZ2u2Jv/knWpBWzLr2UyoZWPF2dWBgfyvLkCK6MDjrVQe50pbUtGAaE+rqTUVTL8qe+4ImbUrhuQiQWmx2TYchROSG6SYK6EL3lyGZ481ZdpW74tN79bJsVDq+H3S/AsW1gctHd4ibfD8OmdHmLwGZX7Dpaxeq0EtZnlFDTZMHH3ZklCWGsSIlgxphAnJ1M5/y9uhYLGzJKmR8XSoCXK2vSinl0VSbvfXsGI2V5Xoguk6AuRG+pOQG7n4d5v9L93Iv2gVewPpbWmyoOtyXW/Vd3iwtLakusu/GSE+tOZ7HZ+eJIJWtSS9iYWUp9q5VAL1eWJoWxIjmCySMDMF1gJr4vv5q39xTwh+uScTIZvPzFMTKKavnLjSkyexfiEkhQF6KvPHcl2G3w7S96L5nudK0NkP6u3nsvywB3PxjX9cS607VYbGzNqWBNWjGbs8posdgJ83VnWXI4K1IiSIn0u+he+pNbcjlYUHMqk/61HccJ9XVncUJYt8YlxGDXb4K6YRhLgH8ATsCLSqk/nvX6COBlIBioBr6plCq82D0lqIt+rbYIGkph6ER97nzzozDl/ksqAesQpxLrXoBDqxySWHe6xlYrW7LLWXWwmM8PV2C22Rke4Mny5HBWjosgJtTnogFeKcXiv39O0lB/Hr8pBYCNmaVMGhlwqo+8EELrF0HdMAwn4DCwECgE9gC3KKUOnfaed4E1SqlXDcOYB9ytlLr9YveVoC4GjII98OpyuOk1iF7cd+OoL4P9r+l2sPXFpyXW3eGQBL/aZgsbMktZnVrMV3lV2OyKsSHerEiJYEVKxAWPu9ntigazFV93F8rqWpj6/7bw0MJofjB/LDa7orKhldALNKkR4nLSX4L6dOBRpdTitutHAJRSfzjtPZnAYqVUoaF/ra9VSl20uocEdTGgNJTrPXbDgJ3PQvEBWPlkpzqzOVwPJdadrrKhlfUZpaw+WMzu49UAJA31Y2VKBMuSw4nw9zjv32s/Dhfs40aorzu7jlbxjed38vq9U5g1NhillByTE5etiwX17q25XZqhQMFp14XA2T0uU4Hr0Uv01wI+hmEEKqWqemeIQvSw02fC5gZoqe0I6C21es+7tzg5Q9wK/Tg9sS79XYcl1gV5u3H7tBHcPm0ExTXNrE0rYVVqMY+ty+KxdVlMGRnAipRwrkoKP6MOvWEYZ9SaHx7oyf8simbC8CEAvL2ngNd25POf+6bK8rwQp+nNmfqN6Fn4fW3XtwNTlFI/OO09EcDTwCjgc3SAT1BK1Z51r28B3wIYPnz4xPz8/F75DkI4XHs1uuYaeHIczH4Ypn2778ZjbtRBffeLUJYObr6QcgtMvheCYxz2MccqG1mdWsyq1GKOlDfgZDK4IiqIlSkXL1Pb7pOMUlalFvHMrRMwDJ1JX9di4cEF0Q4boxD91YBZfj/r/d5AtlIq8mL3leV3MSg018AXT+iZcVgSNFRAc7VDA+klUQoKdrdVrPsIbGYYOUvP3mOXOazPvFKK7NL6UwG+8GQzrs4m5sYEszJlKPPjQjrV9vWn76ZS2dDKv++eAsA7ewuIDfMhOdLfIeMUoj/pL0HdGZ0oNx8oQifK3aqUyjztPUFAtVLKbhjGY4BNKfWri91XgroYlDb+n95z/0lWl6vCOUxDBRx4XVesqz0B3mEw8U5dsc5vqMM+RinFgYIaVh3UjWYqG1rxcnViUUIYK1MimDk2CJfzFLlpZ7MrnEwGZqudSb/fxLXjh/KbqxMB2Hu8mnHD/M9bJEeIgaZfBPW2gVwF/B19pO1lpdRjhmH8FtirlFplGMYNwB8AhV5+/55SqvVi95SgLgalxko49jkkXqev970Cw6ZBSGzfjclug9xNevZ+ZDMYJohZqpfmR80Bk+MCps2u2Hm0ilUHi1mfUUJdixV/TxeWJoZz9bgIplykyA1AfYuFZouNEB938ioamP/4Nn57dQJ3TB+JxWbHZledWgEQoj/qN0G9J0hQF4Nea73u5558M1z1574ejVZ9DPb9Gw78B5qqIGA0TLpHF7bxDHDoR5mtdj4/XMGq1GI2HSqj2WIjzNf91Bn4pKEXL3LTXiRnwgh/Qnzc2ZJVxo/eOsg7D0wnPqKXWucK4UAS1IUY6BrbDoB4Bep+7p//BRY/Br4RfTsuSwtkrdKz94Jd4OQGCdfq2XvkZIdX0WsyW9mcVc6qg0VsO1yBxaYYFeTFiuRwVo4bSlSI99fe41BxHf/dnc+vlifg6mzi9Z35fH64gqduGS+z9wv48MMPue6668jKyiI2tvurRa2trdxxxx3s27ePwMBA3n77bUaOHNn9gV4mJKgLMZikv6f33L/zlZ4V2ywOS1zrltIMXdAm7W19XC80SRe1Sb4J3Hwc/nE1TWY2ZJayKrWYHXlV2BXEhfuyMiWCFSnhRA7x7NR9Xv3qOFtzyk8l2b224zhers5cP/GiObqXlZtuuomSkhLmz5/Po48+2u37/fOf/yQtLY1nn32Wt956iw8//JC33367+wO9TEhQF2KwOT2Q/+d6XXZ22eN9OqRTWuv1Lx57X4LSdHD1hqQb9PJ8eEqPfGR5XQtr0/UZ+AMnagCYOGIIK1MiuCopnGCfzhf3uenZHQT5uPLP2yYCsDathHHD/Rl6gUI5g11DQwMxMTF89tlnrFy5kuzs7G7fc/HixTz66KNMnz4dq9VKWFgYFRUVUlCok/pL8RkhhKO0B3S7DcLHgU9bExSloCqv281ausXNR8/QJ96lu9Tt/Tekvq2T/SIm6OCeeF23itqcLcTXnbuvGMXdV4ziRFUTq9OKWXWwmF+vyuQ3qzO5IiqIFSkRLE4Iw8/j4qsabz8wjSazDdAlb3/41gG+PXs0P10ci1KKvIrGTi3zDxYfffQRS5YsITo6moCAAPbv38+ECRPOed+sWbOor68/5/m//vWvLFiw4IznioqKGDZsGADOzs74+flRVVVFUFBQz3yJy4jM1IUYTI5s1jP3b74PUQu+/v29pflkW2D/N1Rk66I2yd/QgT8sscc+Nqe0nlWpRaxKLaaguhlXJxNzYoJZOS6C+bGheLh+/R768cpG3F2cCPNzJ72wlhVPf8Ezt05gWXI4VpsdJycs0QAAACAASURBVJMxqGeYy5Yt48EHH2ThwoU8+eSTFBQU8Je//KVb90xISGDDhg1ERuotjjFjxrB7924CAwMdMeRBT5bfhbhcNFXrGfH07+t+7vk7wM1bF7TpD9q7xe37N2R+BLZWnVA38S6dYOfA2fuZH6s4WFDDqlR9Br6iXp+BXxgfyspxEcwaG3zRM/DtTjaaWZNWzIqUCPw9Xfn4YBF//iSHtx+Y1uk9/IGkqqqKyMhIQkJCMAwDm82GYRjk5+ef84vMpczUZfm9eySoC3G5emmRrin/3Z1908/9YpqqIfUt/UtIZY6evSfdqAvb9NDeO+gz8LuOVbE6tZh16aXUNltOnYFfmRLBlFEBOF3kDPzpvsqr5J09BTxx0zhMJl2uNre8gceuSbzoOfqB4rnnnmP//v0899xzp56bPXs2v//975k1a1aX7/vMM8+Qnp5+KlHugw8+4J133nHEkC8LEtSFuFw1n9Q93cMSwWqGdf8DU78NofF9PbIO7bP3/a9C5odgbdF5AhPvhMQbwL3nzpKbrXa253acgW8y2wj1dWNZUgQrx0WQEnnxM/Bn++uGHDKLa8/IpI/w82BBfGgPfYOeNWfOHB5++GGWLFly6rknn3ySrKws/vWvf3X5vi0tLdx+++0cOHCAgIAA3nrrLUaPHu2IIV8WJKgLIaAkFV5dAde9CNGLOprJ9CfNJyHtHdj3KpRngosXJF4L4+9wWDvYC2kyW/k0u5xVB4vZmlOB2WZneIAnK1LCWZkylJiwSzuWp5Ri/uPbmDhiCH+5Ua88fJJRwuSRAWd0pBPiUklQF0JoLXU6O90w4KundR/1G18F1362H6wUFO2H/a9A+vtgaYSgGJhwB6TcDF49myVd22xhQ2Ypq1OL+fJIJXYFMaE+rEgJZ0VKBCMCO7f3b7MrGlqt+Hm4UFrbwrQ/bOFnS2L47pworDY7JbUtDAvoZ//2ot+ToC6EONeeF+H4l3Djv/V1XQn4hvftmM6ntV4vy+9/DQr3gMlF15yfcAeMmQemnq0CV1Hfyrr0ElanFrM3/yQAKZF+rEiJYHlyBGF+7p26j1KKQyV1BPu4EeLjzld5ldz6wi5evWcKs6ODL4tMeuEYEtSFEBfXfBL+nqyz5uf8vK9Hc2HlWbD/dUh7S9ec9x2q+72Pv03Xn+9hRTXNrE3TbWIziuowDJg8MoCVKREsTQy7pGX18roWPj5YzG3ThuPp6swbu/J5dlseH373CoJkeV5chAR1IcTFmZt0BbjRc3VSXX2ZPk8+6sr+t+8OOukvZx0cfEOfzVd2GDFTB/f4q3vsaNzpjlY0sDq1hFWpReRVNOJkMnSRm+RwFnWiyM3Zth2uYE1qMX++IRnDMHj601xKalv4/TWJMnsXZ5CgLoS4NFt+B1/8DR5MA79+XgO9rhhS34QDb0B1ni5LG38NjLsVRszo8V9KlFJkl9azOrWY1WkdRW5mxwSzIiWCBXEheLpeevHOP6zPorimhaduGQ/AM58dISrEm8UJYY7+CmKAkaAuhLg0lhY4sQPGzNXX2x+H0ESIXty347oYpXSnuAP/0Xvw5gZdEz/lVp1cN2RELwxBkVpYy+rUYtakFVNW14q7i4n5caGsSI5gTkxwlzrB2eyK+Y9vZW5sCL9ekQDA23tOMGtsMBGXaU36y5kEdSFE11lb4V9X6LKzS/+on+uPx+FOZ26ErDV6ef7YNv3ciJkw7ha9PN8DXePOZrcrdh+vZk2aLnJT3WjG282ZRfGhrEiJ4IqoIFydv76KXTulFM0WG56uzuRXNTL7L1v53dUJ3D59JC0WG4fL6r+2t7wYHLod1A3DcFZKWR0+MgeQoC5EL7BZwdqsg2FpBrx/L1z/Yv8pP3sxNSd0O9iDb+rleWcPiFsOyTfD6Dng1PN9raw2O1/lVbEmrZhPMkqpa7Hi7+nCkoQwlidHMG10AM6dKFN7uvyqRvw9XPHzdGHToTLuf20v/71vKjOigmhsteLsZODmLP3hByNHBPUK4FXgJaVUloPH1y0S1IXoZSd2waZfwS1v6n7udSX6T+d+nrGtFBTuhdT/QsYH0FID3qG6al3KNyAsuVdWH9qr2K1uq2LXaLYR6OXK0iQd4CeP7HyZ2na1TRY2Z5WxIiUCV2cTz3+ex5NbjvDFz+fi7+mKUkpm8IOII4L6/cDdwFRgN/Ai8LZSqsGRA+0KCepC9LHXr9V13L+1tX8vyZ/O2gq5G3Xt+cMbwG6B4Fhdez7pxl7ZfwdosdjYmlPO6tQStmSX0WKxE+LjxlVJ4axICWf8sCFdqiG/L/8k23MreHBBNAC/+DCd0toWXrprsqO/gugDDttTNwwjDrgH+CbgDbyLnr1/6YiBdoUEdSH62JEtetabeL2eDe9/DRKuAXe/vh5Z5zRVw6GPdGvYgp36uWHTIPlGiL8WvHqnHWhjqy5TuyatmM9yKjBb7UT4uXNVUjjLksMZN8y/y7PtF7cfpbLBzMNLYwH4n3dTSYjw5e4rRjnyK4he4vBEOcMwnIDvAn8BXIBc4O/A80opezfGeskkqAvRjxTtgxfmwcqndMW3gebkcUh/D9Lf1ef0Tc4wZr6evccs1W1se0F9i4UtWTrAbztcgcWmGOrvwfLkcJYnR5A41LfLAd5mV9z76h7GDxvCjxaMxW5X/GpVBteOH8rEEQEO/iaiJzhypu4KXIeerc8DvgBeAiKAHwLblVI3d3vEl0CCuhD9TPFBCInTe+yHVumjcfN+2f/qy1+MUlCaDhnv6drzdYU6wS5miV6RiFoILp0rD9tdtc0WNh0qY01aMV/kVmK1K4YHeLIsOZxlSeEkRHQ9wAMUVDex8ukv+OXyeK6bEElNk5n39xexMiWCYJ9+nidxmXLEnvoEdCC/BbAArwEvKKVyT3vPJHRQ79VDkxLUhejHtv1ZB/YHPgeTSR8164Vqbw5lt+tl+Yz3IfMjaKrUvd9jroLE63QVPmfXXhnKyUazDvDpJXx5pBKbXTEi0JNlbUv08eFdC/A2u8JmV7g6m9iYWcq3Xt/Hh9+dwfjhQyiobqKopplJI4Zccoa+6BmOCOpWYBM6Qe7j8x1vMwzDC3haKXV3N8d7SSSoC9HP2Szg5KL/fGoCjLsN5jzc16PqGptVn3vP+ACyV0NLrc4diF2h8whGze61AF/daGZDZinr0kv4Kq8Km10xMlDP4K9K6nqAB31cbtgQT0wmgyc2HeapT3PZ938LCfBypaC6CS83ZwK8eud7inM5IqiPUErlO3xkDiBBXYgBwtyoK9ONnKm7q7XW6z34UbMHTtb86axmOPqZDvA566C1Dtz9IXa5LnAzek6fB/irknSA784SfUOrlbSCGmZE6Xa3P3jzALuPVbHzkfkYhkFVQysBXq5yZK4XOSKoHwUmK6WqznreH9ivlOr59kgXIEFdiAFq13Ow/mfwwHYIT+7r0XSPtRXyPtXlaXPW6wDv5qeT6+JX6l9iXHpnZ/J8AX5EoCdLE/UefHeS7AAyimopqmk+VYN+2ZPbiRziwXO36xhjsdlxkWX6HuWIoG4HwpRS5Wc9HwqcUEr1WTaFBHUhBihLi+6wFrdcX+96DpzdYeKdfTuu7rK2wtGtOpcge40+7ufiBWMXQtwKGLsI3H17ZSjVjWY2Zpay9rQAPyzAg6sSw1maFE5KZPfKyiqleHdfIX4eLixOCMNiszP9D1t44Mox3H9ln831Br0uB3XDMK5r+/E94F6g9rSXnYD5wFylVIyDxnrJJKgLMQgopYvYuPvCTa/p56yt/b9K3dexWeD49o4A31gBTq56yyFuOUQvBZ/QXhlKe5Ld2rYkO6tdH5NbkhjGVUlhXS50c7q6FgtPbcllTkwIV0QFUVLbzM3P7+Sxa5KYOTbIQd9EdCeot585V8DZ/7UtwHHgIaXUGgeMs0skqAsxSCil993dvKG+FP45DVb8Q+9PDwZ2GxTs1sE9azXU5AMGRE6G2GX6ETS2V4bSXlZ2XXoJ23MrMdvshPq6sSQhjCWJ4UwZdemlas8nt6yeP6zP5pGlsYwN9WHX0Sqe2ZrHY9ckMixgAB1x7Gccsfx+DL2nXunowXWXBHUhBqHaQt3Tfc7PIWA0VB/TmeYR4/p6ZI6hFJRlQPY6yFkLJan6+cAoiF6ij8sNm9orzWbqWix8ll3OuvQStuZU0Gq1E+jlyqKEUJYkhjNjTKDD9sg3HSrj8Y05vP+dGXi5ObM6tZjdx6r5xbK4LrWkvVxJ61UhxMC2+kFdp/1/cgZO+dlLUVMAhz/RSXbHPte16N399T589BKImg8eQ3p8GI2tVrbmVLA+o4TPsstpNNvwdXdmQXwoSxPDmTU2yKHB9+lPc1mTVsL6H83CMAxe35mPzWbnLilfe1FdCuqGYfwE+KdSqqXt5wtSSj3R/WF2jQR1IS4DLbX6+NuYefp6/cMQOQmSbujbcfWEljqdSX94A+RugKYqMJz0zH3sQp1oF5rQ48cAWyw2tudW8klGKZuzyqhttuDp6sTc2BCWJIQxNzYEb7furySc3kHuW6/tpdVq59V7pgDwwudHiQr1Zm5MSLc/ZzDpalA/BkxSSlW1/XwhSo60CSF6jaUZXl4MMcv08rxS0HxSt38dbOw2/cvM4Q26q1xpmn7eJwLGLtDlakfP6fFseovNzo68Kj7JLGVjZimVDWZcnU3MjApiSUIYC+JDHVaMptVqw83ZCZtdccUfP2VpUhi/XpGAUop/bctjXmwIsWG9c3qgv5LldyHE4KKUzix3doXjX+rM+ds/0IVtBrO6Yn0MMHeTPjbXWqebzkRO0Uv0UfMhLEWX5O0hNrtiX/5JNmSW8klGKUU1zZgMmDwygCWJYSxKCGOov2PO5NvsiiazFR93F4prmpn15894dGUCt08bQUOrlff3FbI0MYwQ396pw99fSFAXQgxeJ4/rM+7tTWOObQdLk57F9mBw63M2i86mP7IZ8rZ0JNt5BMCYubom/Zi54BfZY0NQSpFZXMeGzFI2ZJZyuKwBgKShfixOCGVRQhhjQ7wdVm2ursWCyTDwdnPm88MV3PHybv5731RmRAVRUN3EwYIa5sWG4OWAbYH+rDt76p0ie+pCiH7jzVt129Tv79VBXamBWYb2UjWU69l73qf60VCmnw+M0gF+9Gy9ktGDCXfHKhtPBfgDJ2oAGBXkxaJ4HeDHD/Pv9ln4052oaiLMzx1XZxMvfXGM3605xM5H5hPm586h4jqqGluZPjpw0DWi6c6eemfInroQov+wWaDmBASO0XvSz8+GiXfD5Hv7emS9RykoP6SD/NGteovC0ggYEJ7SFuCvhOHTeqxHfFldC5sOlbEhs5QdeVVY7YogbzcWxoewKD6M6WMCHZpJb7XZyS6tJ3GoPh3xyAdprEkt4cCvFuLsZGLP8WpcnUykDPN32Gf2FVl+F0JcnpqqYd1PIeFaXcGttQEKd+uZ6+Uwe29nNeuEu2Pb4Og2KNyjj82ZnCFigp7Bj5ypM+x7IMjXtVjYmlPBhsxStuVU0NBqxdPVidnRwSyMD2VebAj+no5tftNktnK0ovFUkL/puR00m22s/oHOu9iaU86wAE/GBPfMLzU9SYK6EEIA7P03rHkQ7v8Uhk7s69H0HXMjFOzS+QfHPofiA6BsOsiHj4MRM2DEFTB8qsOX61utNnbkVbHxUBmbD5VRXt+Kk8lg8sghLIwPY2FcKMMDHV9trrKhlYr6VuLCfVFKMfmxLcwYE8iTt4wHYH16CSnD/IlwUJJfT5Jz6kIIAbqe/JHNumKbYehWsPVlsPRPl9fM/WytDTrI538J+V/pWb3NDBgQEg8jpsPw6Xom7z/MYR9rtyvSimrZdKiUTYfKTiXaxYT6sCA+hPlxoYyLdOw+fLuC6iYsNjujg72pbbIw7ncbeXB+ND9aMBarzc6q1GKujA4myLv/9R+Qc+pCCHE+G34BdUVw4yv6umgfhCWDk0ufDqvPWZr1v0X+V3Bih86yN+uAi+9QGDZFB/hhUyA0yWF94/OrGtmcVc6mQ6XsOX4SW9s+/LzYYObHhTJrbBCero7PbFdKkVfRiLebM2F+7qQW1HD1M1/y1C3jWZESQWVDK1/kVjI3NgQ/j77/f0OW34UQ4kLas+Mbq+CJOJj6ACz6XV+Pqn+xWXWt+oJd+nFiF9QV6tec3SFivK7wN3SSblDjN7TbH1nbZGHr4XI2Z5WzNbuc+lYrrs4mZowJZH5cKPNjQ3psqdxuV2SX1hMZ4IGvuwsfHSjiwbcPsu6Hs4iP8OVwWT2HiutYnBCGh2vv16yXoC6EEF/HbtNFXYLG6sz5ihzY/BtY/HvdVEacqbZIJx0W7NF/lqS2LdkDPuE6ZyFifMefHl3POrfY7Ow5Vs3mrHI2Z5VxoroJgPhwX+bHhTAvNoSUHlqmB10EJ6ukjvhwX0wmg79tOszTnx0h9deL8HZz5ssjlZTUtnDd+KE9NobTOSSoG4ZxDfATIL7tqSzgCaXUhw4ZZRdJUBdC9IicT2DtQ/DANvAKgqo8cPXutf7nA461FUozoGivXrov2gdVRzpeDxitM+0jxulkvPDkLjXn0UvlDWzJKmdLVjl786uxKwjydmV2tA7ws6KD8HXvuWVym11xrLKRqBCdOf+Tdw6y62g1Xz6sexN8sL8QpeD6iT1T+McRrVcfAv4f8Bqwo+3p6cA3gV8qpf7qoLFeMgnqQogeY7d3VKV76zYdqH6cCSZpE9opzSd1Zn37o+hAx7I96EAflqwDfFiK/tP70pq31DSZ2Xa4gk+zy9maU0FtswVnk8HkkQHMiw1hbmwwY4IdV9XufOx2RXl9K2F+ulztrS/sxGQY/Oe+qUBHPXtHcURQLwF+pZR64azn7wd+q5QKd8hIu0CCuhCiV1TmQvVRiF6sr9+6TbdFnXB7345roGmogNJUKD4IJQehJA1q8jte9w6FsCQITWx7JOgtkU4kL1ptdg4U1LAlq5zPssvJKasHYFiAB3NjQpgbG8L00Y4tenM+SinqW634uruglMJss/e7oF4PjFdKHTnr+SjggFLKxyEj7QIJ6kKIXtdar4N63AqYcr+uYnd4g26L6qBM8MtK80koTdfL96XpUJaucxra9+idXCEoWh+vC43XfwbHgt+wi9b3LzzZxNacCrbmlPPFkUpaLHbcnE1MHxPI3JgQ5sQEMyLQq5e+pOM4Iqi/DmQqpf541vM/B5KVUrc5ZKRdIEFdCNFn2jPns9fBW7fAre/omfzpy/aia2wWvTpSlqmDfHkWlB06c/nexQuCY3SAD47Rj6BoGDLynC2SFouNXceq2ZqjZ/HHq3Sy3aggL2ZHBzMnJphpvTCLdwRHNHTxAX4M7KJjT31a2+MJpdRvHTfcSyNBXQjR52xWOPqZLj/r5AxfPQ0Z78Odq3ustvplq7lGN+wpz+r4s/Iw1Jd0vMfJVTeyCRoLgWM7/gwccyoL/3hlI1tzytl6uIIdeVW0WvUsftroQK6MDmZ2dDBjgr16dC++q6ShixBC9Kb093Tlumuf1dcH/ws+YTBmXt+OazBrrtHBvfKwXrqvPKxn+ieP6xK47TyDdHAPGAOBoyFgNK2+I9lT58+nx1rZericoxWNAAz192gL8EHMiOrZjPpLIefUhRCirygFT0/WyV83/ls/dzIfhozo23FdLqxmHdgrD0N1nj6aWJWnkx7ri898r8cQGDKKJq9hHLUFsa/Oj88rvMg1B1BmBJE0LIgro4OZNTaI5Eh/nHrhTPr5SFAXQoi+ZDVDS40+rlVfpivXLfodTP9eX4/s8mZu1AG/+ihUH4OTx/TPJ/OhtgDs1lNvtWOiyhTIcWsAhSqISqcQPENGETF8LLGx8YQPi+q1rZaLBfVOF9E1DCMauAEYDpyR3qmUuqdbIxRCiMHM2bXj/LWLh24g074UX7QfNj8Ky/+ml4VF73H10kfmQhPOfc1m1X0BavKh5gSmmhMEn8zHvzqfhOpjuDXtxKncBuVA27yy2ckbq3cE7oHDcRkSqevk+0boCntj5vVK06BOBXXDMJYB7wMHgInAHmAM4AZs77HRCSHEYOPuq4/BtWuqgoZyXbUOdBOVxkqIXSZFbvqSk7PeIjlrm8Sl7YHNiqovpij/CEdys6koysNcXUBIdSXhJ48Q6bQXf1UDgHL3x3g4/5yP6Amdnan/FviNUuoPbWfWbweKgdfpyIb/WoZhLAH+ATgBL57niNxw4FXAv+09Dyul1nX2/kIIMeCMXagf7fa+rAN77HJ9XX0M/CKlc1x/4+SM4T+cSP/hRKboVRez1c7Bgho25lbwxZFKsgorCVQniaSZ16x2XJ17/phjZ8+pN6DPox81DKMauFIplWEYRhKwVik1vBP3cAIOAwuBQvRs/xal1KHT3vM8upjNvwzDiAfWKaVGXuy+sqcuhBhUbFa95Bs4RifZPTNFZ2rf+pZ+vf1svOj36los7DpaTUF1E/fMHOWw+zpiT70ecG/7uQSIAjLa/v6QTt5jCnBEKXW0bVBvAVcDh057jwJ82372Q68GCCHE5cPJuWNvXSlY8Ci4eOprSzP8czrM/V9IvqmvRig6ydfdhYXxvdsAqLNBfRcwEx2A1wKPG4aRAlxL55ffhwIFp10XAlPPes+jwEbDMH4AeAELOnlvIYQYfEwmvbferrlGdznzaWu3cfI4bH8CZv4YAhw3ExQDV2cX+H8C7Gz7+VFgI3A9cAS4r5P3ON960dlr/7cAryilIoGrgNcNwzhnjIZhfMswjL2GYeytqKjo5McLIcQA5xsON74Co2bp6/IsyPigYzm+NB2y1+oSq+Ky1KmZevuSedvPTcB3uvBZhcCw064jOXd5/V5gSdvn7DAMwx0IQh8aOH08zwPPg95T78JYhBBi4ItZCj872tFEZv/rcPAN+GmeTqyrzAXPQPAM6Ntxil5zSal4hmHMMwzj+22PS613uAcYaxjGKMMwXIGbgVVnvecEML/ts+LQ+/gyFRdCiAs5vSvc4sfg3o3g0pYCtebH8OqKjteba3p3bKLXdfac+ijgAyCJjtl1hGEY6cD1p8/kL0QpZTUM4/vABvRxtZeVUpmGYfwW2KuUWgU8BLxgGMaP0Uvzd6mBXvJOCCF6i5PLmYVUFv6mI5ArpZPs4lfq4jftz0km/aDS2SNtn6ID8e1KqRNtz7WfKVdKqT7rUiBH2oQQohOsZtjzgm5PGrVAB/tnpsLSP0LCtX09OnEJLnakrbPL79OBH7YHdIC2n3/c9poQQoj+zNlV15qPajtUZG6A0bPBr63MSEkqvLxU9ywXA1Zng/oJwOM8z7tz5jE1IYQQA4FfJFz3PERO1NctdTrQt5erzVkPH35H9uEHmM4G9YeAJw3DmGYYhlPbYxrw97bXhBBCDGSjZsG3t3c0nqkthIJd4Oajr/e9Cp8+pvfhRb91wUS5thrvp//Xcwe+BOxt1ybABrxBRxU4IYQQg8GU+2HyfR2JdCUH9bn49uvtT+jjchPv7LsxinNcLPv9+702CiGEEP3P6Znxy/8GdlvHde4m3cGsPah/8r96th+ztHfHKM5wwaCulHq1NwcihBCinzu9Few96zsq15mbIGu1LnITs1Q//9F3YNI9MGJG34z1MtXZ2u8YhuEG3AbEo5flM4E3lVKtPTQ2IYQQ/Vl7O1hXT3gwDexWfV1bAMe/hLi2wjcn82HD/8KchyEsqW/GepnoVKJcWxvUXOAJdBOW9iS5w22V34QQQlzODKMjyAeMhp8cgti2oF5XrOvSt7fyOLoVXrtaB3vhUJ3Nfv8HcAAYrpSapZSaBQwHUtHBXQghhOhgGLrLHMCI6Xom317tztIMTdUdx+d2PQf/mgmtDfra3CRZ9l3U2eX3K4DJSqm69ieUUnWGYfyCju5tQgghxNeLWXpmQp13CITEgpu3vt7wv3o2/8MD+peDqjzwGCKNaTqhs0G9BfA/z/N+ba8JIYQQXZNw7ZmlaqMW6CX89uz71T+C1np4YJu+zl4LPmEwdGLvj7Wf62xQX41utHI/HTPz6cBznNtpTQghhOi6uOVnXs/9BViaOq7X/Uwv6V//or7e8AudZR+7rPfG2E91dk/9R+hEue3omXkLsA04DDzYM0MTQggh0AE8an7H9be2wrxf6p9tFjj0MZSkdVw/PRkOvKGvldIJeZfJHn2nZupKqRrgasMwooA4wAAOKaWO9OTghBBCiHN4B3f87OQCP87oKIzTWq8T8tr332tOwD+SYcU/YOJd0HwSMj7Qe/q+Eb0+9J72tTN1wzBcDMMoNQwjQSl1RCm1Wim1SgK6EEKIfqO9MI5nANz4SkcinpsPLHsCRl2pr4sPwtqfQOXhtusD8J/roaLt2tyom9sMUF8b1JVSFsDCmXXghRBCiP7PMwAm36sT7wBGz4EfpcGwqfq6pQ4aysDFXV8f+hj+OExn3AMU7YfdL+hjdgNAZ/fUnwIeMQyj0xXohBBCiH7HMHTNepe2buKjZ8O3vwD/tr7y4eNg/q86rnM3wfqfdRTO+eppeO7KjhK55VlQsKd3v8NFdDZIzwJmA0WGYWQAjae/qJRa6eiBCSGEEL0uNF4/2s3+md6Lb5/JewVDUExH9bwdT8PhjfDTXH29/XHdtnb53/R1VR54h3acwe9hnQ3qlcD7PTkQIYQQot8xDPAJ7bhO+YZ+tJv9cxh/e8d1Sx00VXVcr30IFvwaIsb3/FgBQw3wNP9JkyapvXv39vUwhBBCiHOd2KWb2Lh6OuyWhmHsU0pNOt9rl7RHbhjGGPSRNtBH2o52d3BCCCHEoDV8aq9+XKeCumEYgcBLwErA/v/bu/cwy6ryzuPfHzRg1DHAgIjcGhWMIhiY1iAXAQXF0YDIqCgGCYmQmUHAjBoQo0BiHC5RQ7w8GpR2EmlUAoiMiii0XESlQSVcRAgy0qLQ3AyNys13/ti77NOnq6hTt1Pdu76f5zlPISJxNQAAFWpJREFUn733Omu/tXpXvWfty1orVudC4LCqunfMD0uSpKEY9O73M4Dn0Nww96T29VJga+CfZiY0SZI0EYOefn8l8PKquqpn3ZVJjgC+Mf1hSZKkiRq0p76MvsfYWr8CPPUuSdJqYNCkfhLwkSSbjaxo3/99u02S1FHnnXceSfjRj340LfVddtll7LTTTsybN49zzjlnWupUY9CkfgzwIuD2JLcnuR24Hfgj4Kgk1428ZiZMSdJsWbRoEbvtthtnn332tNS35ZZbsnDhQt785jdPS31aYdBr6n6VkqQ5aPny5Vx55ZVceuml7LfffpxwwglTrnP+/PkArLXWoP1KDWrQqVdPnOlAJEmrn/PPP599992Xbbfdlg033JBrr72WnXbaaZVyu+++Ow8++OAq60877TT23nvvYYQqJjj4jCRpblm0aBHHHHMMAAcddBCLFi0aNalffvnlww5NozCpS5JGde+993LJJZdw/fXXk4THH3+cJJxyyikkWamsPfXVg0ldkjSqc845h0MOOYRPfvKTv1u3xx57cMUVV7D77ruvVNae+urBuxQkSaNatGgRBxxwwErrDjzwQM4666wp1Xv11Vez+eab88UvfpEjjjiC7bbbbkr1aQVnaZMkaQ0yqVnakvzloDuoqg9NJjBJkjR9nuia+tsHrKMAk7okSbNszKReVVsPMxBJkjQ13ignSVJHDPxIW5INgX2BLYF1e7dVlZO6SJI0ywZK6kl2Bv4v8DCwMfAzYNN2+XacqU2SpFk36On3U4HPAZsBvwFeRtNjXwKcPDOhSZKkiRg0qe8AfLSah9ofB9arqruAvwJOmKHYJEnSBAya1B/peX8XsFX7fjnwzGmNSJIkTcqgN8pdC7wI+DGwGPjbJJsAbwGum5nQJEnSRAzaUz8euLN9/15gGfCPwAbA4TMQlyRJmqCBeupVtaTn/TLgVTMWkSRJmpSBeupJPpxkp5kORpIkTd6gp9//CFiS5KYk70kyf+ZCkiRJkzFQUq+qXYBn0zyr/hbg35NcnuSIJBvMZICSJGkwA4/9XlU/qaq/rarn09wJ/13gr1lxA50kSZpFk53QZR1gPZox4B+fvnAkSdJkDZzUk2yb5MQktwBXAM8F3glsMlPBSZKkwQ06ocsSYEfgh8AngLOq6hczGZgkSZqYQUeU+zrwJ1V100wGI0mSJm/Qu9/fMx0JPcm+SW5OcmuSY0fZ/uEkP2hfP07ywFT3KUnSXDFmTz3J6cBxVfVQ+35MVXXUeDtKsjbwMWAfYClwdZILqurGnnre0VP+7TSn/CVJ0gCe6PT79jR3uY+8H0sNuK8XA7dW1W0ASc4G9gduHKP8m4D3D1i3JElz3phJvar2Gu39FGwG3NGzvJRmpLpVJNkK2Bq4ZIzth9NOJLPllltOQ2iSJK35Bh37ff/29PlUZJR1Y/XyDwLOqapRn4Gvqk9V1YKqWrDxxhtPMSxJkrph0OfUFwF3JflEkl0mua+lwBY9y5sz9mh0B7X7lCRJAxo0qW8CvAt4DnBZktuS/E2S505gX1cD2yTZOsm6NIn7gv5CbZ0bAFdNoG5Jkua8QR9pe7CqzqyqfWh62x+lmVP9xiTfG7COx4AjgYuAm4AvVNUNSU5Ksl9P0TcBZ1fVoDfgSZIkIJPJnW1P+4+B9wI7VNVUr7dP2oIFC2rJkiWztXtJkoYqyTVVtWC0bROa0CXJXknOAO4CzgC+D+w99RAlSdJUDTr2+2nAG4Gn05w+PwL4UlU9PIOxSZKkCRh07PddgA/SXOu+bwbjkSRJkzTu6fck69AMGnORCV2SpNXXuEm9qh4FXsHgw8FKkqRZMOiNcucCr5vJQCRJ0tQMek39p8B7k+wOLAEe6t1YVR+a7sAkSdLEDJrUDwXuB3ZoX70KMKlLkjTLBkrqVbX1TAciSZKmZkKDz0iSpNXXoIPPnP5E26vqqOkJR5IkTdag19S371teB/iD9vPXTmtEkiRpUga9pr5X/7okTwI+DVw+3UFJkqSJm/Q19ar6DfAB4PjpC0eSJE3WVG+U2xh46nQEIkmSpmbQG+X+sn8VsClwMPCV6Q5KkiRN3KA3yr29b/m3wDLgTJrZ2yRJ0ixz8BlJkjpiUtfUk8xL4rV0SZJWI0+Y1JO8PMkb+tYdCywHHkjytSTrz2SAkiRpMOP11I8FNh9ZSPJi4O+AfwbeDbwQH2mTJGm1MF5S3x74Vs/y64FvV9Xb2ulWjwL2m6ngJEnS4MZL6usDd/cs7wp8rWf5amCz6Q5KkiRN3HhJ/efAswGSrAfsCFzVs/0/AQ/PTGiSJGkixkvqXwVOSfIy4GTgIVYe630H4NYZik2SJE3AeM+pvw84F/gGzR3vb62qR3q2HwZcPEOxSZKkCXjCpF5V9wAvTfL7wPKqeryvyOtpkr0kSZplg44o98sx1t83veFIkqTJmuosbZIkaTVhUpckqSNM6pIkdYRJXZKkjjCpS5LUESZ1SZI6wqQuSVJHmNQlSeoIk7okSR1hUpckqSNM6pIkdYRJXZKkjjCpS5LUESZ1SZI6wqQuSVJHmNQlSeoIk7okSR1hUpckqSNM6pIkdYRJXZKkjjCpS5LUESZ1SZI6wqQuSVJHmNQlSeoIk7okSR1hUpckqSOGmtST7Jvk5iS3Jjl2jDJvSHJjkhuSnDXM+CRJWpPNG9aOkqwNfAzYB1gKXJ3kgqq6safMNsBxwK5VdX+Spw8rPkmS1nTD7Km/GLi1qm6rqkeAs4H9+8q8DfhYVd0PUFV3DzE+SZLWaMNM6psBd/QsL23X9doW2DbJlUm+k2Tf0SpKcniSJUmWLFu2bIbClSRpzTLMpJ5R1lXf8jxgG2BP4E3AGUnWX+VDVZ+qqgVVtWDjjTee9kAlSVoTDTOpLwW26FneHLhzlDJfqqpHq+onwM00SV6SJI1jmEn9amCbJFsnWRc4CLigr8z5wF4ASTaiOR1/2xBjlCRpjTW0pF5VjwFHAhcBNwFfqKobkpyUZL+22EXAvUluBC4F3lVV9w4rRkmS1mSp6r+svWZZsGBBLVmyZLbDkCRpKJJcU1ULRtvmiHKSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkdYVKXJKkjTOqSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkdYVKXJKkjTOqSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkdYVKXJKkjTOqSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkdYVKXJKkjTOqSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkdYVKXJKkjTOqSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkdYVKXJKkjTOqSJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSRww1qSfZN8nNSW5Ncuwo2w9NsizJD9rXnw8zPkmS1mTzhrWjJGsDHwP2AZYCVye5oKpu7Cv6+ao6clhxSZLUFcPsqb8YuLWqbquqR4Czgf2HuH9JkjptmEl9M+COnuWl7bp+Bya5Lsk5SbYYTmiSJK35hnb6Hcgo66pv+cvAoqp6OMlfAJ8FXrZKRcnhwOHt4vIkN09jnBsB90xjfV1gm6zM9liZ7bEy22NltseqptomW421IVX9eXVmJHkJcEJVvbJdPg6gqj44Rvm1gfuq6veHEuCK/S6pqgXD3OfqzjZZme2xMttjZbbHymyPVc1kmwzz9PvVwDZJtk6yLnAQcEFvgSSb9izuB9w0xPgkSVqjDe30e1U9luRI4CJgbeAzVXVDkpOAJVV1AXBUkv2Ax4D7gEOHFZ8kSWu6YV5Tp6q+Anylb937et4fBxw3zJhG8alZ3v/qyDZZme2xMttjZbbHymyPVc1YmwztmrokSZpZDhMrSVJHmNR7jDeMbdcl2SLJpUluSnJDkqPb9RsmuTjJLe2/G8x2rMOUZO0k309yYbu8dZLvtu3x+fbGzzkhyfrtGBI/ao+Tl3h85B3t78v1SRYledJcOkaSfCbJ3Umu71k36jGRxunt39jrkuw0e5HPjDHa49T2d+a6JOclWb9n23Fte9yc5JVT3b9JvdUzjO2rgOcDb0ry/NmNaugeA/5XVT0P2Bn4n20bHAt8s6q2Ab7ZLs8lR7PykxgnAx9u2+N+4M9mJarZ8Q/A16rqD4AX0rTLnD0+kmwGHAUsqKoX0NwEfBBz6xhZCOzbt26sY+JVwDbt63DgE0OKcZgWsmp7XAy8oKp2AH5Me+9Y+/f1IGC79jMfb3PRpJnUV5jzw9hW1c+r6tr2/YM0f7A3o2mHz7bFPgu8dnYiHL4kmwOvBs5ol0MzINI5bZE50x5Jnga8FPg0QFU9UlUPMIePj9Y84PeSzAOeDPycOXSMVNVlNE8r9RrrmNgf+D/V+A6wft+jzGu80dqjqr5eVY+1i98BNm/f7w+cXVUPV9VPgFtpctGkmdRXGHQY2zkhyXxgR+C7wCZV9XNoEj/w9NmLbOg+Arwb+G27/J+BB3p+QefScfIsYBlwZns54owkT2EOHx9V9TPgNOCnNMn8l8A1zN1jZMRYx4R/Z+Ew4Kvt+2lvD5P6CoMMYzsnJHkq8K/AMVX1H7Mdz2xJ8hrg7qq6pnf1KEXnynEyD9gJ+ERV7Qg8xBw61T6a9lrx/sDWwDOBp9CcYu43V46R8czl3x+SHE9zmfNzI6tGKTal9jCpr7AU6J1AZnPgzlmKZdYkWYcmoX+uqs5tV981coqs/ffu2YpvyHYF9ktyO83lmJfR9NzXb0+1wtw6TpYCS6vqu+3yOTRJfq4eHwB7Az+pqmVV9ShwLrALc/cYGTHWMTFn/84meSvwGuDgWvEs+bS3h0l9hXGHse269nrxp4GbqupDPZsuAN7avn8r8KVhxzYbquq4qtq8qubTHA+XVNXBwKXAf2uLzaX2+AVwR5LntqteDtzIHD0+Wj8Fdk7y5Pb3Z6RN5uQx0mOsY+IC4JD2LvidgV+OnKbvsiT7An8F7FdVv+rZdAFwUJL1kmxNcwPh96a0LwefWSHJf6XpiY0MY/uBWQ5pqJLsBlwO/BsrriG/h+a6+heALWn+iL2+qvpvjOm0JHsC76yq1yR5Fk3PfUPg+8Bbqurh2YxvWJL8Ic1Ng+sCtwF/StM5mLPHR5ITgTfSnFb9PvDnNNdF58QxkmQRsCfNzGN3Ae8HzmeUY6L94vNRmju9fwX8aVUtmY24Z8oY7XEcsB5wb1vsO1X1F23542musz9Gc8nzq/11Tmj/JnVJkrrB0++SJHWESV2SpI4wqUuS1BEmdUmSOsKkLklSR5jUpTVQksVJPjoL+z08yU+T/DbJCWOUeUaSryd5KMlAj9ckOTTJ8rGWJQ3GpC4NKMnCJJXkjFG2ndJuu3Ca97lnW+9G01nvJGPZgGYmw1NpnsM+bYyi76QZMvUPgU5N1iGt7kzq0sTcAbyxncgEgHY40D+hGWSjy7aiGf/9wnZGv7F60s8BrqmqW9pR6CQNiUldmpjrgFuAN/SsezXwG2Bxb8EkayX56yR3JHk4yb8l2b9n+/y2F35gkouT/CrJjUn2GdlOM9wowLK27MKeXayV5O+S3JPk7iSnJVmrp/7XJbkuya+T3JfkW0k2GesHS7JlkvOSPNi+zm2nniXJoTQjowHc1sYyf5Q6bqeZ4OSQ3nifqO5BJTkiya1JHmn/fVvPtpOTfLVn+W3t/t/Ys+7KdvSusepf2H+mJckJSa7vL5PkvUnuSrI8yZlJfm8iP4s0U0zq0sR9mmZYxxGHAWey6uxKRwPvohnzeXvgPODcdqjVXh8ATgdeSDMHwdlpZsq7AziwLbMdzanso3s+dzDN0JK7AEcCx9AMV0qSZ9AMU/pZ4Hk086D/81g/UDt85/nAJjQT1+xFcwr9/Hbb52mG9oRmvudNWXnKyBEvAr5BM0TopsDRA9Q9riQH0Awv+hHgBcA/AB9P8sdtkcXAblkxicqewD3tvkjy5Da2xYPsbxx70PxfvZzm/+cVwMnTUK80dVXly5evAV7AQuBCYAPg1zSTLzwDeJhmjOuFNKemR8r/DHhfXx2LgX9p38+n+SJwRM/2zdp1u7XLe7bLG41Sz1V96y4Gzmjf79R+bqsBf7Z9gMeB+T3rnkUzB8De7fKCts7549R1IbBwgnUfCizv2d6/fCXNfAz9/x9XtO+fCjwKvKRdXkozLezNPTE8BKwz3v9v37oTgOv7yjwAPLVn3VvaY+Aps32M+vJlT12aoKq6n6bXfRjNDFSLq2ql6+lJnkbTG72y7+NXAM/vW3ddz/uRaRefPkAo1/Ut39nzuR/S9JivT/KvSf57ko2foK7nAXdW1e0jK6rqtrbO/ngnajrqfh5P0JbVXN+/FtgzyTbA02h69lsmeSbNl6NvV9Wj7aWA5T2v90zw57muVr6f4CqaCW6ePcF6pGk3b/wikkbxGZpT28uB9z1BudEe6epf9+jvNlRVe0Z6kC/cj/Yt18jnqurxJK8AdqY5PfxnwAeT7FFVPxylrowR62jxTtR01T1eWy6mOd1+D3B5VS1P8j2ahL4n8JW23J00d+aPGJlR7rdtrL3WmUB80qyzpy5NzjeBR2imVzy/f2NV/QdN8titb9NuNPNtD+qR9t+1JxpgNa6qqhNpriffSXvNfRQ3Apv13vyWZorZZ04w3pmq+ybGb8vFwK40p9oX96x7NT3X06vqsaq6tec1ktSXseojeP33PwBs3/v0A80Xp0eAfx/wZ5FmjD11aRLaHvUONNMXjzVP9qnASUluAa6hufa6O/BfJrCr/0fTG311ki8Dv66xHyX7nSQ7A3sDF9HM6bwjsAVjJ9Fv0Jyy/1ySo2h6rP9Ic0r7kgnEO1N1nwp8Mck1wNdpbto7GHhdT5nLaU6Dvw44pV23GDie5pr398bZxyXAu5McBlzW1rMrzfX5XvOAzyQ5ieaLyf8G/qmqHhrwZ5FmjD11aZKq6sG2Rz6W02mS0SnA9cABwIFV9YMJ7ONnwPtp7pC/i+Y68SB+SZOQLqR5BO/vgb+pqn8ZYz8FvJamt7qY5lG6XwCvbbdN2nTUXVXnA28H3kHzxeRo4H9U1Zd7yiyn+fL0ECsev7uK5gmBb1dV/+WK/n1cBJxI09bX0NzI+PFRin4LuKH9Oc6j/TIwyM8hzbRM8fdVkuaM9rn7jarqNbMdizQae+qSJHWESV2SpI7w9LskSR1hT12SpI4wqUuS1BEmdUmSOsKkLklSR5jUJUnqCJO6JEkd8f8Bcx8TdYEWSpwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(model_surv_0, linestyle=\":\", c=\"C0\")\n",
    "ax.plot(model_surv_1, linestyle=\":\", c=\"C1\")\n",
    "\n",
    "ax.plot(surv_ip_0, c=\"C0\")\n",
    "ax.plot(surv_ip_1, c=\"C1\")\n",
    "\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.74, 'A = 1');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survival estimates\n",
      "     smoking cessation: 80.7%\n",
      "  no smoking cessation: 80.5%\n"
     ]
    }
   ],
   "source": [
    "print('survival estimates')\n",
    "print('     smoking cessation: {:>0.1f}%'.format(surv_ip_1[-1] * 100))\n",
    "print('  no smoking cessation: {:>0.1f}%'.format(surv_ip_0[-1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create 500 boostrap samples to get confidence intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_results(boot_data):\n",
    "    model = sm.GLM.from_formula(\n",
    "        'event ~ qsmk + qsmk:time + qsmk:np.power(time, 2) + time + np.power(time, 2)',\n",
    "        freq_weights=boot_data.weight,\n",
    "        family=sm.families.Binomial(),\n",
    "        data=boot_data\n",
    "    )\n",
    "    results = model.fit()\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def month_preds(results):\n",
    "    A0_pred = results.predict(\n",
    "        pd.DataFrame({'time': list(range(120)), 'qsmk': [0] * 120})\n",
    "    )\n",
    "    A1_pred = results.predict(\n",
    "        pd.DataFrame({'time': list(range(120)), 'qsmk': [1] * 120})\n",
    "    )\n",
    "    return A0_pred, A1_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def survival_difference(boot_data):\n",
    "    results = model_results(boot_data)\n",
    "    A0_pred, A1_pred = month_preds(results)\n",
    "    surv_ip_0 = survival_curve(A0_pred)\n",
    "    surv_ip_1 = survival_curve(A1_pred)\n",
    "    min_diff = min(s1 - s0 for s1, s0 in zip(surv_ip_1, surv_ip_0))\n",
    "    end_diff = surv_ip_1[-1] - surv_ip_0[-1]\n",
    "    return min_diff, end_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell will take a while to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [02:26<00:00,  3.41it/s]\n"
     ]
    }
   ],
   "source": [
    "min_diff_samples = []\n",
    "end_diff_samples = []\n",
    "nrows = pt_data.shape[0]\n",
    "for i in tqdm(range(500)):\n",
    "    boot_data = pt_data.sample(nrows, replace=True, axis=0)\n",
    "    min_diff, end_diff = survival_difference(boot_data)\n",
    "    min_diff_samples.append(min_diff)\n",
    "    end_diff_samples.append(end_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "difference in final survival probability\n",
      "  est   CI\n",
      " 0.2%   (-5.0, 4.2)\n"
     ]
    }
   ],
   "source": [
    "estimate = (surv_ip_1[-1] - surv_ip_0[-1]) * 100\n",
    "ci_lo, ci_hi = np.percentile(end_diff_samples, q=[2.5, 97.5]) * 100\n",
    "\n",
    "print('difference in final survival probability')\n",
    "print('  est   CI')\n",
    "print(' {:>0.1f}%   ({:>0.1f}, {:>0.1f})'.format(estimate, ci_lo, ci_hi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "largest difference in survival probability\n",
      "   est   CI\n",
      " -1.9%   (-6.2, 0.1)\n"
     ]
    }
   ],
   "source": [
    "estimate = min(s1 - s0 for s1, s0 in zip(surv_ip_1, surv_ip_0)) * 100\n",
    "ci_lo, ci_hi = np.percentile(min_diff_samples, q=[2.5, 97.5]) * 100\n",
    "\n",
    "print('largest difference in survival probability')\n",
    "print('   est   CI')\n",
    "print(' {:>0.1f}%   ({:>0.1f}, {:>0.1f})'.format(estimate, ci_lo, ci_hi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The previous two confidence intervals differ from the book's, but they also change from run to run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Section 17.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Program 17.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps will be similar to Program 13.3, with calculation of survival curves similar to the previous program. However, survival curves will be estimated for each individual, and the final curves will be averages of those curves\n",
    "\n",
    "1. outcome modeling, on the original data (aka \"block 1\")\n",
    "2. prediction on expanded dataset _per individual_, see below)\n",
    "    - block 2: a copy of the dataset with `qsmk` set to zero\n",
    "    - block 3: a copy of the dataset with `qsmk` set to one\n",
    "3. create the survival curves from the predictions, _per individual_, see below\n",
    "4. average the individual curves to get marginal survival curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = sm.GLM.from_formula(\n",
    "    'event ~ qsmk + qsmk:time + qsmk:I(time ** 2) + time + I(time ** 2) + sex'\n",
    "    '+ race + age + I(age**2) + C(education) + smokeintensity + I(smokeintensity ** 2)'\n",
    "    '+ smokeyrs + I(smokeyrs ** 2) + C(exercise) + C(active) + wt71 + I(wt71 ** 2)'\n",
    "    '+ smkintensity82_71',\n",
    "    family=sm.families.Binomial(),\n",
    "    data=pt_data\n",
    ")\n",
    "res = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>event</td>      <th>  No. Observations:  </th>  <td>176764</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>  <td>176739</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>        <td>Binomial</td>     <th>  Df Model:          </th>  <td>    24</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>         <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -2092.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 22 Jan 2020</td> <th>  Deviance:          </th> <td>  4185.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>21:49:17</td>     <th>  Pearson chi2:      </th> <td>1.74e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>11</td>        <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>              <td>   -9.2724</td> <td>    1.379</td> <td>   -6.723</td> <td> 0.000</td> <td>  -11.976</td> <td>   -6.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(education)[T.2.0]</th>    <td>   -0.1401</td> <td>    0.157</td> <td>   -0.895</td> <td> 0.371</td> <td>   -0.447</td> <td>    0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(education)[T.3.0]</th>    <td>   -0.4335</td> <td>    0.153</td> <td>   -2.841</td> <td> 0.005</td> <td>   -0.733</td> <td>   -0.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(education)[T.4.0]</th>    <td>   -0.2350</td> <td>    0.279</td> <td>   -0.842</td> <td> 0.400</td> <td>   -0.782</td> <td>    0.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(education)[T.5.0]</th>    <td>   -0.3750</td> <td>    0.239</td> <td>   -1.571</td> <td> 0.116</td> <td>   -0.843</td> <td>    0.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(exercise)[T.1.0]</th>     <td>   -0.1469</td> <td>    0.179</td> <td>   -0.820</td> <td> 0.412</td> <td>   -0.498</td> <td>    0.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(exercise)[T.2.0]</th>     <td>    0.1504</td> <td>    0.176</td> <td>    0.854</td> <td> 0.393</td> <td>   -0.195</td> <td>    0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(active)[T.1.0]</th>       <td>    0.1601</td> <td>    0.130</td> <td>    1.232</td> <td> 0.218</td> <td>   -0.095</td> <td>    0.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(active)[T.2.0]</th>       <td>    0.2294</td> <td>    0.188</td> <td>    1.222</td> <td> 0.222</td> <td>   -0.139</td> <td>    0.597</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk</th>                   <td>   -0.0596</td> <td>    0.415</td> <td>   -0.143</td> <td> 0.886</td> <td>   -0.874</td> <td>    0.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk:time</th>              <td>    0.0149</td> <td>    0.015</td> <td>    0.987</td> <td> 0.324</td> <td>   -0.015</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>qsmk:I(time ** 2)</th>      <td>   -0.0002</td> <td>    0.000</td> <td>   -1.367</td> <td> 0.172</td> <td>   -0.000</td> <td> 7.39e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time</th>                   <td>    0.0227</td> <td>    0.008</td> <td>    2.690</td> <td> 0.007</td> <td>    0.006</td> <td>    0.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(time ** 2)</th>           <td>   -0.0001</td> <td> 6.71e-05</td> <td>   -1.750</td> <td> 0.080</td> <td>   -0.000</td> <td> 1.41e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex</th>                    <td>   -0.4368</td> <td>    0.141</td> <td>   -3.101</td> <td> 0.002</td> <td>   -0.713</td> <td>   -0.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>race</th>                   <td>    0.0524</td> <td>    0.173</td> <td>    0.302</td> <td> 0.763</td> <td>   -0.288</td> <td>    0.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>                    <td>    0.0875</td> <td>    0.059</td> <td>    1.481</td> <td> 0.139</td> <td>   -0.028</td> <td>    0.203</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(age ** 2)</th>            <td>-8.128e-05</td> <td>    0.001</td> <td>   -0.149</td> <td> 0.882</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smokeintensity</th>         <td>    0.0016</td> <td>    0.014</td> <td>    0.114</td> <td> 0.909</td> <td>   -0.026</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(smokeintensity ** 2)</th> <td> 7.182e-05</td> <td>    0.000</td> <td>    0.301</td> <td> 0.764</td> <td>   -0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smokeyrs</th>               <td>    0.0168</td> <td>    0.031</td> <td>    0.547</td> <td> 0.584</td> <td>   -0.043</td> <td>    0.077</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(smokeyrs ** 2)</th>       <td>  5.28e-05</td> <td>    0.000</td> <td>    0.124</td> <td> 0.901</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>wt71</th>                   <td>   -0.0622</td> <td>    0.019</td> <td>   -3.270</td> <td> 0.001</td> <td>   -0.100</td> <td>   -0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(wt71 ** 2)</th>           <td>    0.0004</td> <td>    0.000</td> <td>    3.584</td> <td> 0.000</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>smkintensity82_71</th>      <td>    0.0017</td> <td>    0.007</td> <td>    0.259</td> <td> 0.795</td> <td>   -0.011</td> <td>    0.014</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                  event   No. Observations:               176764\n",
       "Model:                            GLM   Df Residuals:                   176739\n",
       "Model Family:                Binomial   Df Model:                           24\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:                -2092.9\n",
       "Date:                Wed, 22 Jan 2020   Deviance:                       4185.7\n",
       "Time:                        21:49:17   Pearson chi2:                 1.74e+05\n",
       "No. Iterations:                    11                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "Intercept                 -9.2724      1.379     -6.723      0.000     -11.976      -6.569\n",
       "C(education)[T.2.0]       -0.1401      0.157     -0.895      0.371      -0.447       0.167\n",
       "C(education)[T.3.0]       -0.4335      0.153     -2.841      0.005      -0.733      -0.134\n",
       "C(education)[T.4.0]       -0.2350      0.279     -0.842      0.400      -0.782       0.312\n",
       "C(education)[T.5.0]       -0.3750      0.239     -1.571      0.116      -0.843       0.093\n",
       "C(exercise)[T.1.0]        -0.1469      0.179     -0.820      0.412      -0.498       0.204\n",
       "C(exercise)[T.2.0]         0.1504      0.176      0.854      0.393      -0.195       0.496\n",
       "C(active)[T.1.0]           0.1601      0.130      1.232      0.218      -0.095       0.415\n",
       "C(active)[T.2.0]           0.2294      0.188      1.222      0.222      -0.139       0.597\n",
       "qsmk                      -0.0596      0.415     -0.143      0.886      -0.874       0.755\n",
       "qsmk:time                  0.0149      0.015      0.987      0.324      -0.015       0.044\n",
       "qsmk:I(time ** 2)         -0.0002      0.000     -1.367      0.172      -0.000    7.39e-05\n",
       "time                       0.0227      0.008      2.690      0.007       0.006       0.039\n",
       "I(time ** 2)              -0.0001   6.71e-05     -1.750      0.080      -0.000    1.41e-05\n",
       "sex                       -0.4368      0.141     -3.101      0.002      -0.713      -0.161\n",
       "race                       0.0524      0.173      0.302      0.763      -0.288       0.392\n",
       "age                        0.0875      0.059      1.481      0.139      -0.028       0.203\n",
       "I(age ** 2)            -8.128e-05      0.001     -0.149      0.882      -0.001       0.001\n",
       "smokeintensity             0.0016      0.014      0.114      0.909      -0.026       0.030\n",
       "I(smokeintensity ** 2)  7.182e-05      0.000      0.301      0.764      -0.000       0.001\n",
       "smokeyrs                   0.0168      0.031      0.547      0.584      -0.043       0.077\n",
       "I(smokeyrs ** 2)         5.28e-05      0.000      0.124      0.901      -0.001       0.001\n",
       "wt71                      -0.0622      0.019     -3.270      0.001      -0.100      -0.025\n",
       "I(wt71 ** 2)               0.0004      0.000      3.584      0.000       0.000       0.001\n",
       "smkintensity82_71          0.0017      0.007      0.259      0.795      -0.011       0.014\n",
       "==========================================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps 2 & 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model gives the information for constructing _conditional_ survival curves.\n",
    "\n",
    "As the text says, \"we can use this model to estimate the survival curves under treatment and no treatment for white men aged 61, with college education, low levels of exercise, etc. However, our goal is estimating the marginal, not the conditional\", pg 218\n",
    "\n",
    "To get the marginal survival curves, we create conditional curves and average them. To do this, we create a full person-time set for each individual twice, once with `qsmk` equal to zero, and once with `qsmk` equal to one. Then we'll create the survival curves for each individual, and average by time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create the individual person-time sets, we'll go row-by-row through the original data, set `death` to zero and `longevity` to 120, i.e. survived through end of follow-up, and set `qsmk` to zero or one.\n",
    "\n",
    "We'll use two arrays to carry all of the survival curves, fill it in for each individual, then average the array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "survivals_qsmk0 = np.zeros((nhefs_all.shape[0], 120))\n",
    "survivals_qsmk1 = np.zeros((nhefs_all.shape[0], 120))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `survival_curve` function below was defined in cell 22 above. For explanation, see the text between cells 20 and 21."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill in the individual survival curves\n",
    "for i, (_, row) in enumerate(nhefs_all.iterrows()):\n",
    "    # the person_time_format function expects a DataFrame, so convert the\n",
    "    # row to a DataFrame first\n",
    "    frame = pd.DataFrame([list(row)], columns=row.index)\n",
    "    frame[\"death\"] = 0\n",
    "    frame[\"longevity\"] = 120\n",
    "\n",
    "    # qsmk = 0 curve\n",
    "    pt_block2_i = person_time_format(frame)\n",
    "    pt_block2_i[\"qsmk\"] = 0\n",
    "    hazard = res.predict(pt_block2_i)\n",
    "    survivals_qsmk0[i] = survival_curve(hazard)\n",
    "\n",
    "    # qsmk = 1 curve\n",
    "    pt_block3_i = pt_block2_i.copy()\n",
    "    pt_block3_i[\"qsmk\"] = 1\n",
    "    hazard = res.predict(pt_block3_i)\n",
    "    survivals_qsmk1[i] = survival_curve(hazard)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 4: Average the individual curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "surv_gform_0 = survivals_qsmk0.mean(axis=0)\n",
    "surv_gform_1 = survivals_qsmk1.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(surv_gform_0)\n",
    "ax.plot(surv_gform_1)\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.76, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot these curves over the previous version, to see how they change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "ax.plot(surv_ip_0, c=\"C0\")\n",
    "ax.plot(surv_ip_1, c=\"C1\")\n",
    "\n",
    "ax.plot(surv_gform_0, c=\"C0\")\n",
    "ax.plot(surv_gform_1, c=\"C1\")\n",
    "\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.76, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a pretty good match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the mean survival curves plotted over some of the individual curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1629/1629 [00:04<00:00, 361.02it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "n_subj_tot = nhefs_all.shape[0]\n",
    "for i in tqdm(range(n_subj_tot)):\n",
    "    ax.plot(survivals_qsmk0[i], c=\"C0\", alpha=0.01)\n",
    "    ax.plot(survivals_qsmk1[i], c=\"C1\", alpha=0.01)\n",
    "\n",
    "ax.plot(surv_gform_0)\n",
    "ax.plot(surv_gform_1)\n",
    "\n",
    "ax.set_ylim(0.5, 1.02)\n",
    "ax.set_xlabel('Months of follow-up', fontsize=14)\n",
    "ax.set_ylabel('Survival probability', fontsize=14)\n",
    "ax.text(95, 0.88, 'A = 0')\n",
    "ax.text(95, 0.76, 'A = 1');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently, there is a _lot_ of variation between individuals"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
